How to Monitor the Web using Frase [GUIDE]

Frase takes you one step further than most media monitoring tools by including automatic summarization and topic extraction.

For every url, Frase Media Monitoring extracts over 15 data points that contribute to a highly structured database used to provide tools that speed up your research.

If you curate content for a newsletter, social media posting or personal research,  it is important to use a tool that keeps track of all of your topics across the web. RSS readers and content aggregators are not new, but Frase provides additional value by summarizing and analyzing your topics.

1. Set up your keywords

Frase allows you enter advanced boolean queries using AND/OR operators. You can also expand each keyword with topics recommended by Frase.  

2. Set up your category filters

Frase automatically classifies all content according to a taxonomy. These categories help remove noise, which is a common problem among content aggregators. Example: you may only want articles about Artificial Intelligence under the health category.

3. Set up your sources

You can chose to monitor from the entire Frase Index (which includes thousands of top publishers) or you can build a custom list of sources.

If a particular source is not available in Frase, you can import it via RSS.

4. Set up your delivery preferences

You can choose to opt in for a daily digest and can also include a list of additional emails you would like to receive it.

You can also download the Frase App to carry monitors with you.  

For the Pro User, you can integrate Frase Monitors with your CMS to automate publishing or consume the content via RSS to build more custom solutions.

5. Preview your personalized Frase Homepage

Content from your monitors can be previewed on your Frase Homepage where you can filter contents by topic and publisher.  You can also bookmark content on your homepage.

How to Compose Newsletters using Frase [GUIDE]

Sending a weekly roundup is a great way to stay in touch and build trust with your audience while becoming a relevant authority in your industry. In addition to sending emails, you can also publish your weekly roundups on your website to contribute to your domain authority.  

Producing a valuable weekly or monthly newsletters is hard, time consuming work and requires you to consistently curate quality content for your readers.

Creating Newsletters with Frase is fast and easy.  This guide covers the workflows for setting up your Frase Monitors, bookmarking, newsletter composition and export.

1. Set up your monitors

Frase lets you set up advanced monitoring system to create a quality feed of content about your specific topics of interest. Get Started with Media Monitoring.

2. Your Frase homepage

Your Frase Homepage will also display the latest content from your Monitors or you can access them from the Frase mobile app.

3. Bookmark your favorite sources

Bookmark your favorite sources as you discover them. Bookmarks can be organized in folders.

4. Create new document

When you are ready to write your newsletter all you have to do is create Document and select Content Curation. Then enter the theme of your newsletter.

5. Add summaries to your document

You will be taken to the editor, where you will be presented with recent news about your newsletter theme. From there you can click and summarize each news article. Clicking Add To Document will add and overview to your document in the editor. You can also access your Monitors and Bookmarks from the editor.

6. Write your personal commentary

Be creative and add your own unique introduction, edit the summaries, and enrich your content with your personal insight.  

7. Export

When you are ready to share your newsletter with your audience you can Export your newsletter in various formats or directly publish it in your email marketing tool (Frase currently supports Mailchimp).

How to Optimize Content using Frase [GUIDE]

Search engines reward publishers who keep their content fresh and accurate. Creating new content is obviously important, but you may find greater ROI by doubling down on your best content and continuously optimizing it. Learn more about how AI is changing SEO and content optimization.

Frase scores your content for a given search query, and recommends specific topics to make your content win at organic search.

1. Create a new document

Select the Content Optimization document type while creating a new document.

2. Import content

Input the URL you want to optimize (for already published content) or copy and paste your content directly. Then enter your Target Search Query (which is the query you want to rank for).

3. Render document and process competitor’s content

Frase will render your content in the editor.  On the right panel, Frase analyzes the top 20 search results for your Target Search Query and benchmarks their topics against yours.

4. Review insights

The right panel will display multiple data points related to content optimization

  • Avg. Word Count
  • Target Score: the top performing score across competitors
  • Your content score
  • List of topics: each topic shows the average number of times the topic was mentioned across results. You will see a color coded score on the left side which tells you the number of mentions for the topic in your content.

5. Identify topic gaps

Be sure to pay attention to those topics where your content is zero (gray color) or orange (below average). Click on a topic to understand where the topic gets mentioned across the results. This will provide context on how your competitors are covering the topic.

6. Update your content with fresh information

Browse Frase generated summaries or read full articles within the tool and directly add content and citations to your document.

7. Review score

Once you are done optimizing your content, click “Topics” to re-evaluate your content score.

8. Export

If you are happy with your updated content score, you can export your new amazing content in multiple formats.

How can artificial intelligence help content marketers research faster?

Since starting Frase in 2016, we’ve been exploring ways to make content marketers more productive through the use of machine learning and natural language processing. At Frase we’ve used the term ” AI-powered Research Assistant” to convey the idea of using AI to augment human research capabilities, while acknowledging that AI is not meant to automate content creation.

After talking to dozens of content marketers, it becomes clear that certain research workflows can be fully automated or at least accelerated through AI-powered tools. When we say ” content marketers” we are typically referring to three segments: (1) freelance content writers, (2) SEOs and digital marketing consultants, and (3) in-house content marketing managers. These segments understand today’s focus on content quality (versus quantity), and the need for deeper research to deliver value to an audience that has endless alternative sources of information. So what are some specific research workflows where content marketers could use some extra help?

As a marketer, it’s time to evaluate your current content marketing process and identify tasks that can be automated to save time and money. As we will discuss in this post,  AI tools can help you identify the best topics and content format to optimize engagement, automatically keep your strategic documents up to date, or generate content briefs that will make outsourcing your content creation tasks much more cost-effective.  Let’s dive right in:

SEO Research Brief

  • Background: if you want people to find your content, you have to think strategically about what topics your content covers. As discussed in other posts, artificial intelligence has changed the way SEO works. Content creators have to think beyond keywords and make sure they cover the most important topics to rank for a query. The challenge comes when you are faced with having to read dozens of search results to understand the key topics driving those stories. In addition to reaching a satisfactory topic coverage, you also have to provide additional value when compared to already existing content.
  • The AI solution: AI-powered tools should be able to scan the top search results for a target query and tell you what are the most important topics driving those results. As a second step, a tool should be able to provide related topics to help you come up with alternative ways to address the overall theme. Remember search engines don’t care about exact matches anymore; they use large word vectors to understand relationships between topics.

Content Optimization

  • Background: optimizing already existing content can be a very efficient way to increase your organic traffic. Content optimization can be used to improve low performing content, or to solidify your position on page 1. Just think about it: given the amount of content getting published every day, even your most forward thinking content is going to get old pretty quickly. Having a consistent content optimization program might be more effective and scalable than continuously publishing new content.
  • The AI solution: AI-powered tools should be able to scan your existing content and benchmark it against your target query. As a result, you should be able to identify topic gaps — where is my content falling behind? This tool should help you enrich and amplify your existing content by incorporating new perspectives and topics. If the gap between you original content and the newest trends is too large, you might want to consider creating a whole new piece of content.

Content Curation

  • Background: keeping up to date with all the new content published in your domain might be challenging; how to filter out the noise? However, content curation programs can be an scalable way to come up with new ideas, create newsletters and share fresh content across social channels.
  • The AI solution: AI-powered tools should help you monitor the media with advanced settings that help you filter out the noise. This tool should automatically summarize news articles to help you consume information faster. Automatic summarization also makes information easier to digest by your audience, for example when delivered as a newsletter.

The topic of how AI is impacting content marketing is being widely discussed. These are some articles that also address key topics mentioned in the post:

Artificial Intelligence–the Next Frontier In Content Marketing ( – Apr 27 2018

  • In this blog, I’ll cover how you can leverage AI to help increase ROI and get better results.
  • AI is an umbrella term to describe a suite of unique, but related, technologies that includes machine learning, deep learning, neural networks, natural language processing (NLP), and natural language generation (NLG).
  • With the ability to process an enormous amount of unstructured data and decipher natural language, AI is used to extract insights and make recommendations based on previously established criteria.
  • This capability allows marketers to fully leverage the power of personalization and marketing automation technologies to deliver targeted content to each prospect or customer and increase the ROI of their content marketing efforts.

Content Marketing and Artificial Intelligence: A Perfect Marriage? – Single Grain ( – Oct 10 2018

  • In recent years, however, AI has started showing enormous potential in content marketing, one of the largest segments of the global digital marketing industry.
  • In this article, I will explore the relationship between the two and discuss how AI can help content marketers, people who are already using this technology today, whether or not it will end our present-day content marketing tactics, and what lies ahead.
  • It can help digital marketers make sense of the ever-increasing amount of data on the web, speed up the content creation process, and take advantage of personalized advertising, among other things.
  • Most AI programs, such as IBM Watson Explorer Deep Analytics Edition  (WEXDAE), use both machine learning and Natural Language Processing  (NLP) to identify hidden insights from the data.
  • AI can help you create content although the scope of content automation goes well beyond just content creation.

AI will fundamentally change how we manage content ( – Aug 26 2017

  • Content management is about to undergo a foundational shift as artificial intelligence and machine learning bring long-sought order to enterprise content.
  • Last week, M-Files, a hybrid content management solution, announced it was acquiring Apprento , a Canadian startup that uses natural language processing (NLP) and natural language understanding (NLU) to provide semantically based intelligent summaries.
  • Greg Milliken, SVP of Marketing at M-Files, says the Apprento purchase gives them an immediate way to process unstructured data in an intelligent way.
  • All of these moves suggest that we could be in the midst of an industry shift that Levie and Patel alluded to, as content management firms try to use intelligence to make sense of the increasingly large amount of content moving into the enterprise.
  • As AI and machine learning evolve, it makes sense that content management is going to play a role in that.

AI + Content Marketing: The Technology that’s Changing the Industry ( – Aug 17 2017

  • AI Technology: Machine learning (ML), a field of artificial intelligence that can help marketers understand massive amounts of data, analyze user intent, and generate enhanced and more customized customer experiences.
  • Several companies have started using IBM’s Watson Analytics machine learning and cognitive computing technology, which guides data exploration, provides insights, and is capable of answering human-posed questions.
  • AI Technology: Natural Language Processing (NLP) focuses on the interactions between humans and machines in an effort to optimize machine voice recognition and reading comprehension.
  • AI Technology: Natural Language Generation (NLG), like NLP, is an area of AI that uses algorithms to translate data into human-like language in the form of auto-reportage, headline production, and more.

How AI Is Shaping the Future of Content Marketing and Personalization ( – Oct 17 2018

  • The practice of collecting basic demographic information from customers to create a successful business marketing strategy is one of the past.
  • By performing in-depth analyses of various patterns in data, marketers create customized experiences for customers.
  • Deeper insights will allow content marketers to effectively predict content performance and patterns in audience engagement.
  • According to recent studies, 60% of content marketers struggle with personalization.
  • 80% of marketers reveal  that personalized content is more effective than generic content.

What is automatic text summarization?

Automatic text summarization is the data science problem of creating a short, accurate, and fluent summary from a longer document. Summarization methods are greatly needed to consume the ever-growing amount of text data available online. In essence, summarization is meant to help us consume relevant information faster.

While summarization has been a field of study for decades, it has certainly grown in popularity in recent years. In 2017, Salesforce announced certain breakthroughs in the field of abstractive automatic summarization, and the use cases have proliferated across the enterprise.

Earlier in 2014, data scientist Juan Manuel Torres Moreno published a full book on the subject titled “Automatic Text summarization”, where he provided 6 reasons why we need automatic text summarization tools:

  • Summaries reduce reading time.
  • When researching documents, summaries make the selection process easier.
  • Automatic summarization improves the effectiveness of indexing.
  • Automatic summarization algorithms are less biased than human summarizers.
  • Personalized summaries are useful in question-answering systems as they provide personalized information.
  • Using automatic or semi-automatic summarization systems enables commercial abstract services to increase the number of texts they are able to process.

As described by Agolo, a Microsoft-backed summarization startup,  a document summarizer must generally overcome a set of challenges:

  • Determining which sentences are the most salient.
  • Making the summary cohesive and readable.
  • Minimizing the number of references to ideas and entities not mentioned in the summary. (i.e. coreference resolution).

Types of automatic summarization

Automatic summarization can be used in a variety of applications. Depending on the use case and type of documents, summarization systems can fall into different categories.

Abstractive vs. Extractive

When a human is given a corpus of text to summarize, they might rewrite the main points in their own words. This is called abstractive summarization and it requires high-level human skills like the ability to combine multiple perspectives into coherent natural language. As of 2018, the state of the art for abstractive summarization is not yet up to par, so many automatic summarization systems opt for a technique called extractive summarization.

Extractive summaries are excerpts taken directly from the input documents and presented in a readable way. The summary does not contain any rephrasing of the ideas presented in the original text. Extractive summarization methods employ AI-powered techniques to identify the most important sentences directly from the source.

Illustration of Salesforce’s model generating a multi-sentence summary from a news article. For each generated word, the model pays attention to specific words of the input and the previously generated output.

Single-document vs. Multi-document summarization

When summarizing a single document, the summarization system can rely on a cohesive piece of text with very little repetition of facts. However, the chance of redundancy increases with multi-document summarization systems. An ideal multi-document summarizer maximizes the important information included in the summary while minimizing repetition.

Indicative vs. informative

The taxonomy of summaries largely depend on the user’s end goal. For example, journalists or analysts looking to skim information as fast as possible would be interested in the high-level points of an article. So this use case requires an ‘indicative’ type of summary.

On the other hand, when the reader is looking to get more granular, summaries may require more detail. For example, a summary might need to allow topic filtering to let the reader further drill down the summary. This type of summary is considered to be ‘informative’.

Document length and type

The length of the input text heavily impacts the sort of approaches a summarization system can take. The largest summarization datasets, like Newsroom by Cornell University, have focused on news articles, which usually range 300 to 1,000 words. Extractive summarizers can be very effective when dealing with relatively short documents like news or blog articles. On the other hand, a 20-page report or a chapter of a book can only be summarized with the help of more advanced approaches like hierarchical clustering or discourse analysis.

In addition to length, documents may also fall into different genres. It is very different to summarize a news article to a financial earnings report or a technical white-paper. These are very different types of documents that may require entirely distinct summarization approaches.

Recommended articles

As a recap, here is a list of articles that cover the basics of automatic summarization. These articles were actually summarized by Frase’s summarization engine, which uses AI-powered extractive summarization.

New AI Breakthrough from Salesforce Research Boosts Productivity with Text Summarization (

  • Salesforce Research is tackling this exact challenge and today we’re excited to announce two new breakthroughs in natural language processing towards the goal of automatically summarizing a long text and serving up coherent, digestible highlights that help you stay informed in a fraction of the time.
  • Text summarization is a very tough challenge, especially for longer texts such as news articles, and the work we are doing at Salesforce Research is pushing the state of the art.
  • I’m honored to work with Caiming Xiong and Richard Socher to introduce a more contextual word generation model and a new way of training summarization models with reinforcement learning (RL) .

Introduction to Automatic Text Summarization ( – Jan 05 2017

  • Without an abstract or summary, it can take minutes just to figure out what the heck someone is talking about in a paper or report.
  • Automatic text summarization is part of the field of natural language processing , which is how computers can analyze, understand, and derive meaning from human language.
  • By keeping things simple and general purpose, the automatic text summarization algorithm is able to function in a variety of situations that other implementations might struggle with, such as documents containing foreign languages or unique word associations that aren’t found in standard english language corpuses.

A Gentle Introduction to Text Summarization ( – Nov 28 2017

  • Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.
  • After reading this post, you will know: Why text summarization is important, especially given the wealth of text available on the internet.
  • Automatic text summarization, or just text summarization, is the process of creating a short and coherent version of a longer document.
  • These deep learning approaches to automatic text summarization may be considered abstractive methods and generate a wholly new description by learning a language generation model specific to the source documents.

Taming Recurrent Neural Networks for Better Summarization (

  • Abstractive approaches use natural language generation techniques to write novel sentences.
  • In the past few years, the Recurrent neural network (RNN) – a type of neural network that can perform calculations on sequential data (e.g. sequences of words) – has become the standard approach for many Natural Language Processing tasks.
  • The decoder’s ability to freely generate words in any order – including words such as beat that do not appear in the source text – makes the sequence-to-sequence model a potentially powerful solution to abstractive summarization.
  • Explanation for Problem 2 : Repetition may be caused by the decoder’s over-reliance on the decoder input (i.e. previous summary word) , rather than storing longer-term information in the decoder state.

An Overview of Summarization – agolo ( – Nov 03 2016

  • A summarization system with what’s called a generic trigger will find the most important topics in a given input text and summarize it without further guidance.
  • A generic trigger for summarization is useful in cases where the user does not yet know the contents of the text to be summarized.
  • Agolo’s summarizer takes these factors into account at various points in the summarization process.

A Quick Introduction to Text Summarization in Machine Learning ( – Sep 18 2018

  • Text summarization refers to the technique of shortening long pieces of text.
  • Machine learning models are usually trained to understand documents and distill the useful information before outputting the required summarized texts.
  • With such a big amount of data circulating in the digital space, there is need to develop machine learning algorithms that can automatically shorten longer texts and deliver accurate summaries that can fluently pass the intended messages.
  • However, the text summarization algorithms required to do abstraction are more difficult to develop; that’s why the use of extraction is still popular.
  • As research in this area continues, we can expect to see breakthroughs that will assist in fluently and accurately shortening long text documents.

How to Make a Text Summarizer – Intro to Deep Learning  (YouTube)

13 blockchain projects to help content creators and freelancers

Independent content creators are struggling to monetize their content at acceptable rates. Advertisers battle with fraud, ad blockers and declining advertising revenue. Publishers are finding it hard to monetize content through paywalls.

Blockchain promises solutions to remove unnecessary middlemen between freelancers and employers, prevent plagiarism, provide efficient escrow services and allow new types of reward programs.

Given the growth of the freelance economy, it seems like these solutions – many of them early-stage startups – are addressing a large, growing market of content creators.

Source: Dorie Clark, Harvard Business Review


If you are one of the 81 million people who use WordPress to publish content, you’ll soon be able to automatically timestamp your works within the WordPress content management system using the new plugin. (

February 8, 2018 marked the launch of Frost , an open API and set of developer tools from that will enable content publishers and developers to more easily register their creative works on the blockchain. The new API will enable integrations and decentralized applications, including the WordPress plug-in. Developers can get instructions on how to make an account, create an API key, read developer documentation and access the javascript library at the Frost website. (


The media sharing and advertising platform Snapparazzi has announced a release of its minimum viable product (MVP) of a blockchain-based platform. The app aims to allow everyone with a smartphone “to become a reporter” or a content creator. The user takes footage or a photo of newsworthy events with their smartphone and shares it using the platform. The interested buyers — TV, newspapers, radio, etc. — pay for the content in fiat currency. The user will in turn be paid in SnapCoin, the platform’s token for their contribution. The platform also targets content creators and says they can get paid substantially more with Snapparazzi compared to Youtube for creating or watching content. (


Currently, the most popular decentralized content platform is Steemit , a blogging and social networking website with over one million users. The platform rewards publishers by the popularity of their content as determined by upvotes. The Steemit ecosystem involves three types of tokens, namely, steem, steem power and steem dollars. (

When publishers create popular content, they earn Steem dollars, a stable currency that can be sold on the open market at any time. The steem token creation depends on the interaction in the Steemit platform and is usually distributed to content creators and curators either as steem power or steem dollars. Just like bitcoin, people can buy the crypto for speculation purposes. Steem and steem dollars can be converted to steem power to increase voting power. (

ASQ Protocol

Another project with an even bigger user base is the ASKfm-backed ASQ Protocol . Just like Steemit, the platform enables content creators to get paid for the value of their work rather than the revenue they generate from advertisements placed on their content. With this project, consumers can order their desired content from ASQ-supported platforms and pay with ASQ tokens. Brands can also sponsor content and reward users who engage with it. (


WOM is the creation of YEAY – an App that provides teens and young adults with a location to shoot and share videos featuring streetwear styles. The app makes the videos shoppable by incorporating affiliate links. Brands may benefit from user-generated product recommendations and creators earn WOM Tokens as rewards for the value driven through their videos. To-date YEAY reports it has received $7 million in seed funding from investors including the former COO of Airbnb, the former CEO of Deutsche Telekom, Grazia Equity, Mountain Partners, and others. (


You42 says creators will be offered “dynamic commerce capabilities” enabling them to sell content through their own page – effectively creating a “personalized marketplace” that meets their meets. They would generate an income through the U42 token – a cryptocurrency that has been specially created for the platform. (

Users and creators would also be able to use the platform’s internal currency, UCoin – which can be earned for certain activities or purchased via U42 tokens – to access premium content or tip content creators for work they appreciate. (


A new promising project called AC3 hopes to make life easier for content creators and educators. It’ll allow them to share their work with their audience directly, and use a more secure and transparent system to avoid plagiarism. (

Unlike many other blockchain companies, AC3 didn’t go down the ICO route when funding their project and chose to focus instead on building the technology. The platform is straightforward: fans and followers pay with AC3 tokens to access content, and creators can also sell their content for these same tokens. They can be used as currency to buy things like design and programming courses within the platform. (

Microsoft and Ernst & Young

Microsoft and Ernst & Young (EY) announced the launch of a blockchain solution for content rights and royalties management on Wednesday. The blockchain solution is first implemented for Microsoft’s game publisher partners. Indeed, gaming giant Ubisoft is already experimenting with the technology. After successful testing, Microsoft and EY hope to implement the solution across all industry verticals which require licensing of intellectual property or assets. (


The model implemented by the C3C blockchain, for instance, creates a direct consumer-to-creator network, replacing intermediaries in the process. Artists, writers, and musicians currently share somewhere from 10% to 20% of all the revenue they make on a particular online platform. The amount goes markedly up when you add other expenditures such as payment processing fees, bank charges and value added taxes. A payment processor, for instance, will take some 3% to 6% of any payment made to a content creator while cryptocurrency payments eliminate these charges. (

Blockchain provides complete control not only over the copyright but also empowers creators to price their content dynamically and perform micro-metering activities. Dynamic pricing is among the greatest benefits of blockchain, enabling prices adjusted by demand, advertiser-support, and many more factors. (


The community-owned open source social networking platform Minds has amassed one million users and has recently launched a cryptocurrency reward program based on the ethereum blockchain for all users on the platform. (

Minds is also introducing a direct peer-to-peer advertising tool that allows users to offer tokens to one another in exchange for shares of specific content. (Image: Minds) (


Escrow services, which hold money independently from two parties until all terms are satisfied, could help alleviate some of the stress between freelancers and employers. StratusCore added a Digital Escrow service to its platform — which was built using blockchain technology — last year. With the service, employers and freelancers can agree to the terms of a project and the necessary benchmarks or milestones that must be met to ensure it is completed on time. (

When an agreement is reached, an employer deposits the funds for the project into the StratusCore Digital escrow account. Funds are disbursed from the escrow account directly into a freelancer’s preferred bank account once the necessary deliverables are met and the digital assets are uploaded to the platform. This occurs within one to two days after the employer digitally signs off on the delivered asset and the escrow funds are released. (

ARA Blocks

Among other factors, the sometimes necessary presence of middlemen importantly shrinks and limits the profits of content creators. Luckily for them, there is now a new platform that promises to make things easier, more profitable and secure, and all this thanks to the powerful blockchain technology: meet ARA Blocks . (

With this platform, content creators will be able to sell and distribute their content directly to their targets, thus eliminating the need for the aforementioned middlemen and effectively increasing their revenue. And, given that it is built using Ethereum (which uses blockchain ), all this is done with all the technology’s landmarks, namely security and traceability. (

ACG Networks’s DApp

ACG Networks’s DApp store would allow content creators of the digital content industry around the world to develop and release their own DAPPs and Ethereum based smart contracts. Since those DApp considered to be tokenized and hence can proliferate within ACG Network public blockchain for voting and forecasting purpose by accessing smart contracts (Ethereum). For an application to be viewed as a DApp with regards to Blockchain , it must meet the accompanying criteria: Application must be totally open-source It must work self-governing, and with no content controlling the larger part of its tokens. (

What is Natural Language Processing (NLP)?

Natural language processing (NLP) is an area of artificial intelligence that helps computers understand human natural language.  Often referred to as the engineering side of computational linguistics, NLP focuses on extracting meaning from unstructured data. NLP includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches.

NLP has removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before. NLP enables real-world applications, including:

  • Automatic summarization: the process of creating a short and coherent version of a longer document. (
  • Sentiment analysis: the process of determining the emotional tone behind a series of words, used to gain an understanding of the the attitudes, opinions and emotions expressed within an online mention. (
  • Named entity recognition (NER) locates and classifies the named entities present in the text. NER classifies entities into pre-defined categories such as the names of persons, organizations, locations, quantities, monetary values, specialized terms, product terminology and expressions of times. (
  • Parts of speech tagging: the process of marking up a word in a text as corresponding to a particular part of speech (noun, verb, adjective, etc.), based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase , sentence , or paragraph. (

Subcategories of NLP include natural language generation (NLG) — a computer’s ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language. (

Business applications of NLP

NLP has been widely applied across many industries. Some examples include:

  • Enterprise question answering tools leverage NLP to enhance customer experience and improve administrative activities by allowing users to ask questions in everyday language about products, services or applications and receive immediate and accurate answers.  Many companies are successfully using customer support chatbots to streamline some of the work that would traditionally fall to representatives. Models built with NLP algorithms are the brains of these chatbots. They’re trained using text data from past conversations between your customer support agents and customers.
  • Optimizing Customer Satisfaction: manually sifting through product reviews and surveys can be prohibitively time consuming, but NLP can be used to build data models that generate insights to help optimize customer satisfaction. Sentiment analysis is used to classify text into positive, neutral, or negative categories.
  • Classifying medical records: Researchers at MIT in 2012 were able to attain a 75 percent accuracy rate for deciphering the semantic meaning of specific clinical terms contained in free-text clinical notes, using a statistical probability model to assess surrounding terms and put ambiguous terms into context. (
  • Ad placement: NLP can help in intelligent advertisement targeting and placement. Media buying is usually the largest channel in an organization’s advertising budget. So, it is important to ensure that the advertisement reaches the right eyeballs. Browsing behaviors, social media and emails contains a lot of information imbedded that can give a lot of insights about consumer preferences. NLP can be used here to match keywords of interest in the texts to target the right consumers. It can also be used for disambiguation or identification of the sense in which a word is used in a sentence.
  • Reputation monitoring: with increased competition in diverse market, monitoring reputation is essential to avoid getting drifted away in the tide. With a plethora of information sources abut companies like social media, blog posts, and reports, it becomes imperative to utilize these sources to get more insights about the reputation and reviews of the company. NLP is the best way to understand and extract insights from these sources.
  • Helping hiring manager: NLP can help hiring managers to filter resumes. Automated candidate sourcing tools can scan CVs of applicants to extract required information and pinpoint the candidates who are right fit for the job. This will save a lot of time and give a more efficient solution.
  • Market intelligence: NLP can help to monitor and track market intelligence. Since markets are influenced by information exchange, using event extraction, NLP can recognize what happens to an entity.

These are only a few examples of the many ways NLP can be used to unlock valuable information from text data. To learn how to get started implementing these techniques in your business, below are some resources that might help you dive deeper into the world of NLP:

Relevant Wikipedia articles

  • Natural language generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations.
  • Computational linguistics is an interdisciplinary field concerned with the statistical or rule-based modeling of natural language from a computational perspective, as well as the study of appropriate computational approaches to linguistic questions.
  • Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.
  • Textual entailment (TE) in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text.

  • The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). Also known as the vector space model. In this model, a text (such as a sentence or a document) is represented as the bagof its  words, disregarding grammar and even word order but keeping multiplicity.

Open Source NLP Libraries

These libraries provide the algorithmic building blocks of NLP in real-world applications.

  • Apache OpenNLP: a machine learning toolkit that provides tokenizers, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and more.
  • Natural Language Toolkit (NLTK): a Python library that provides modules for processing text, classifying, tokenizing, stemming, tagging, parsing, and more.
  • Standford NLP: a suite of NLP tools that provide part-of-speech tagging, the named entity recognizer, coreference resolution system, sentiment analysis, and more.
  • MALLET: a Java package that provides Latent Dirichlet Allocation, document classification, clustering, topic modeling, information extraction, and more.

NLP Courses

In case you are looking to get your feet wet with NLP, these are 2 popular online courses for beginners:

  • Stanford Natural Language Processing on Coursera: “This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.”
  • Udemy’s Introduction to Natural Language Processing: ” This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you’ll get hands on experience working with and analyzing textAs a student of this course, you’ll get updates for free, which include lecture revisions, new code examples, and new data projects.”

NLP YouTube videos

  • Natural Language Processing with Deep Learning – Stanford University
  • Introduction to Natural Language Processing – Cambridge Data Science Bootcamp
  • Natural Language Generation at Google Research

5 ways freelance writers can use artificial intelligence

The demand for online writing services has exploded in the past decade. Every organization needs a website, every website needs content, and content needs writers to create it. Entire business models live online. By many accounts, the bulk of the world’s content has been created in the past few years.

According to data from research firm Technavio, global spending on content marketing—which includes freelance writing services—will increase at a compound annual growth rate of 16% through 2021 to a projected total of $412 billion.

Anyone would think it is a great time to be a writer. However, the reality is not so straightforward. Making a decent living out of writing remains tough.

The New Economy

It’s not just freelance writers who are experiencing the uncertainty of the digital economy. The march of automation is leaving many other workers apprehensive for the future of their jobs.

According to freelance marketplace Upwork, the majority of workers in the U.S. economy will be freelance by 2027. That is a stunning prediction by any standard. “Freelancers increasingly think that having a diversified portfolio of clients is more secure than one employer,” said Upwork in its recent survey of the status of freelancing in the United States.

According to UpWork, the trend toward freelance is unstoppable

So if the economy is increasingly turning freelance and the demand for content is plentiful, isn’t this a great time for wordsmiths everywhere?

The outlook is mixed. Although 2016 data from the Bureau of Labor Statistics put the median writer salary at $61,820, other surveys point to much lower pay for freelance writers, exacerbated by low hours.

Take the 2017 survey from Freelance Writing in which the average freelance writer reported working fewer than 20 hours a week for less than $10,000 a year. Most of these low-income writers had fewer than three years’ professional experience, with 70% saying that finding enough work was their main struggle.

By contrast, survey respondents earning $40,000 or more a year and working more than 40 hours a week had at least five years’ experience and picked up most of their work through networking and word of mouth.

The Role of AI

The pattern is clear: making a sustainable living as a freelance writer is possible, but it takes time to get established. The question is how can writers best navigate that tricky period when they are finding their feet and building their credentials?

Here we turn to AI. If artificial intelligence is nipping at the heels of conventional employment, it may also be the thing that helps freelance writers gain more jobs, increase their productivity, and generate a higher wage.

What follows are five ways in which AI can turn you into a more accomplished and productive writer, regardless of experience.

1. Topic Ideation

It’s easy to run out of topic ideas. There is so much content online and so many things already written about, how will you find fresh topics to run with?

AI has an answer for that. Topic analysis tools provide great ways of reviewing existing content and identifying gaps. Through natural language processing, these tools analyze huge swathes of content within seconds and generate recommendations for your review. Platforms such as MarketMuse or Frase can help in providing topic recommendations that also incorporate SEO value.

You’re still making the final call about what topics to select and why. But AI guides you along the way in a fraction of the time.

2. Accelerate your Pitching

As the Freelance Writing survey shows, the majority of writers are saddled with the need to pitch multiple topics to secure one job. Even highly experienced writers must spend time preparing topic pitches to ensure buy-in from their clients.

The problem is pitching takes time. A draft headline alone won’t cut it. You will likely need to provide an article outline and provisional research to highlight what your planned article is about. You may also need to demonstrate how the article is relevant in term of target audience and SEO.

In those cases where a prospective client supplies the topic and invites proposals, the pressure on the quality of the pitch is even higher. Freelance writer Rachel Brooks says she pitches at least 10 times per day to guarantee a steady stream of work.

The good news is that AI-driven summarization tools can help not only cut down the amount of time associated with pitches but improve the quality of the pitches themselves.

A good quality pitch also makes it much easier when the time comes to create the content. Content strategist Fernando Nikolic has taken this technique to the next level. He supplies his international team of freelance writers with research-rich creative briefs using the same AI summarization techniques that are helping Rachel speed up her pitching process. AI-generated article outlines get the content creation process rolling in minimal time and keep it on track.

3. Optimize your Content for SEO

Selecting a good topic and putting together a strong pitch are necessary components, but what happens if your content is not optimized for search? It’s like the tree that fell down in the forest that nobody heard.

In past times, editors would help guide content development for you. Now the machines can do much of the hands-on work. Platforms such as Can I Rank ensure your content is optimized for search. They also help keep your writing on topic and free from digression. This leaves more time for you to polish up the final piece.

And just think how satisfying it is to see your article rise to the top of the search engine rankings for the keyword query you targeted. That’s tangible ROI by anyone’s measure.

4. Creating emotionally-optimized content

So you’re checking all the best practice boxes, but what about voice, personality, and emotion?

Your content needs to resonate with its audience. Sometimes just a small change in wording or the way you structure the opening of an article will determine whether or not a person reads on. AI platforms like Persado offer great functionality for optimizing your content emotionally.

Headlines are imperative here. A smart headline that immediately conveys its meaning while generating a level of curiosity that propels a reader to click through will win out.

And although clickbait headlines might be tempting, remember the actual content of your piece must deliver against the promise of the headline. Don’t oversell your story, but don’t underplay it either.

5. Improve your Writing Style and Grammar

Finally, don’t forget the plethora of tools available, from Grammarly to Acrolinx, to help you writer cleaner and more readable prose. When was the last time you opened a paper dictionary or thesaurus? You have everything you need online to render your writing as strong, as clear, and as optimized for search as possible.

Making AI accessible

While freelance writers understand the potential of AI-powered tools, many of these solutions are not easily affordable. AI companies often target large companies and deliver enterprise products at enterprise-level prices, leading to questions around accessibility for freelancers.

By contrast, less expensive tools may fall short in the eyes of professional writers, who need solutions that actually improve the quality of their work.

Frase meets the needs of freelance writers by offering an enterprise-grade research and content optimization solution at a price point that freelance writers can understand. Freelance writer Rachel Brooks agrees. Find out more about her experience as reported in the nDash blog

Save time, write better, secure more work. Frase offers AI-driven solutions that help freelance writers thrive at every stage.

Can AI make you a faster writer?

Sorry for the spoiler, but the simple answer to the question is yes — artificial intelligence can absolutely make you a faster writer. There’s little doubt that AI-driven tools and platforms are able to speed up most aspects of the editorial creation process.

Just think how easy it is nowadays to auto-correct text, to validate facts, or search for synonyms online. It used to take serious time and effort to thumb through dictionaries, encyclopedias, and thesauruses. This information, digitally stored in the cloud, is now delivered at lightning speed through our devices.

So the question isn’t whether AI can save you time in the editorial process — it’s more about how artificial intelligence impacts different stages of the content creation process, and what you do with the time it frees up. And the research on this point is interesting.

Reinvesting the gains 

For the most part, content creators appear to be investing time savings back into the editorial process—rather than speeding up and finishing faster, bloggers are spending longer on their blogs than ever before.

The data is clear. Results from Orbit Media’s 2017 survey of bloggers found the average time spent creating a blog post was 3 hours 20 minutes, an increase of approximately 40% since 2014. By a similar measure, the average blog post in the 2017 survey came in at 1142 words, also increasing by around 40% from three years earlier. The heyday of the short post is gone.

So the time savings derived from advances in technology—if only from improvements in semantic search and word processor functionality—are being invested back into the content on a surprisingly consistent basis. But why?

The gravitation toward longer blogs in recent years has certainly something to do with search engines increasingly rewarding extended content. In a city of tall buildings, you need to build your structure higher than the others to stand out.

One further thesis is that blog posts are getting longer because it has simply become easier to compose a lengthy article than it was a decade ago.

Think about it for a moment—importing new material and adding visual media into your word processor is “plug and play” nowadays in most content management systems. And for every new element you add, there’s an opportunity for further comment. Bloggers are continually encouraged by the system to make their pieces longer.

Compounding this, nobody is really telling the writer when to stop. Sure, there might be a word count of sorts. But, unlike print, there is no fixed endpoint for digital articles. Once you start writing, you could theoretically go on forever. Not that we would advise that, of course.

Whatever happened to writer’s block? 

Once upon a time, writers would script movies and pen books about not being able to write. French novelist Flaubert used to famously agonize for hours over a single word. Writer’s block was a thing even not so long ago, but we seem to be hearing less about it these days.

Why might that be? For one, the process of getting started on your creative work is increasingly painless. Numerous topic analysis tools are available to help get your content research underway, while advances in natural language processing are enabling machine learning platforms to generate deep research at the click of a button. Editorial AI is like a personal assistant, gently nudging, suggesting, and prodding you along the way.

It’s also easier than ever to work with others on editorial projects. If the typewriter was an island technology, the personal computer and the internet moved everyone a step closer together.

Since the advent of cloud technology, the floodgates have opened. Digital solutions support every aspect of creative collaboration from ideation and scheduling through development and review.

There has been a similar proliferation in the available tools for optimizing, repurposing, scaling, personalizing, and distributing content both within domestic markets and internationally across languages. As part of the ongoing explosion of martech platforms, AI-led tools can schedule social posts for optimal times, uncover traits of top-ranking content,  AB-test landing pages, and manage paid search and paid social campaigns. They can offer live recommendations on improving content performance, assist with design and pagination, and automatically recognize images. IBM Watson, for one, is on a mission to hoover up all the unstructured data on the internet and give it form.

What this all means is that it is easier than ever to produce insightful, engaging content supported by AI. At the same time, this has had the parallel effect of making it even more difficult for your content to stand out from the crowd. Experts have talked for several years now about the requirement for 10x content and the almost Herculean levels of effort needed to differentiate your editorial.

Human versus machine

And it is not as though creative jobs are beyond the reach of the machines. Many aspects of the content and copywriter’s job are already subject to some degree of automation.

The Associated Press has long used AI writing technology to produce data-heavy sports and financial reporting, and NLP firms are working hard and fast to offer organizations AI solutions for automated report writing.

Or look at advertising copy. In launching its proprietary AI copywriter, retail giant Alibaba insisted that the introduction of the technology would allow advertising creatives to spend more time on higher-end analysis and tasks. “Copywriters will shift from thinking up copy—one line at a time—to choosing the best out of many machine-generated options, largely improving efficiency,” the company said in a statement.

Nancy A. Shenker, founder and CEO of marketing firm theONswitch, expects AI technology to play a growing role in content development in the coming years. “My estimate is that 50% of all content will be developed by machines, with oversight and editing by humans,” , she told EContent Magazine. “Artificial intelligence will recommend topics based on trends, gather facts—and validate them—and assemble very tight posts and suggested graphics based on those combinations.” Flesh and blood creatives will add “soul and humor as needed,” according to Shenker.

Can machines get creative?

The march of AI is inevitable. 2017 research from McKinsey found that across the global workforce approximately 50% of “current work activities are technically automatable by adapting currently demonstrated technologies.”

As always, the critical question is how we harness advances in technology to our collective betterment or ill.

For now, the reality is that humans are not getting sidelined in the creative process. Some might say we are even entering a golden age of AI-assisted creative.

While AI is great at aggregating data, identifying patterns, and generating recommendations, it is not so good yet at coming up with original, well-edited, and emotionally engaging material that helps your content stand out.

The oversight of strategy in content development—why we create what we are create and how the content maps to broader commercial objectives—remains a fundamentally human domain, at least for the time being.

Similarly, the ability to induce an emotional response from a piece of content, or to decide whether something is fit or not to publish, continue to rely on human judgement.

Throw into your content some proprietary research, take a standpoint on your topic, and inject some voice and style, and you’re doing things that machines still find difficult.

The interplay between human and machine is fluid, fast-moving, and fascinating. An architect who uses 4-D CAD visualization is still 100% an architect and a carpenter who deploys AI to determine the best way to sequence a home-building project based on local weather conditions is still 100% a carpenter. They are just using better tools than were previously available.

And so it goes with writers and other creative professionals. Write faster, write harder, write better.

20 Applications of Automatic Summarization in the Enterprise

Summarization has been and continues to be a hot research topic in the data science arena. While text summarization algorithms have existed for a while, major advances in natural language processing and deep learning have been made in recent years. Many internet companies are actively publishing research papers on the subject. Salesforce has published various groundbreaking papers presenting state-of-the-art abstractive summarization. In May 2018, the largest summarization dataset as revealed in a projected supported by a Google Research award.

While there is intense activity in the research field, there is less literature available regarding real world applications of AI-driven summarization. One of the challenges with summarization is that it is hard to generalize. For example, summarizing a news article is very different to summarizing a financial earnings report. Certain text features like document length or genre (tech, sports, finance, travel, etc.) make the task of summarization a serious data science problem to solve.  For this reason, the way summarization works largely depends on the use case and there is no one-size-fits-all solution.

Summarization: the basics

Before diving into an overview of use cases, it is worth explaining a few basics around summarization:

There are two main approaches to summarization:

  • Extractive summarization: it works by selecting the most meaningful sentences in an article and arranging them in a comprehensive manner. This means the summary sentences are extracted from the article without any modifications.
  • Abstractive summarization: it works by paraphrasing its own version of the most important sentence in the article.

There are also two scales of document summarization:

  • Single-document summarization: the task of summarizing a standalone document. Note that a ” document” could refer to different things depending on the use case (URL, internal PDF file, legal contract, financial report, email, etc.).
  • Multi-document summarization: the task of assembling a collection of documents (usually through a query against a database or search engine) and generating a summary that incorporates perspectives from across documents.

Finally, there are two common metrics any summarizer attempts to optimize:

  • Topic coverage: does the summary incorporate the main topics from the document?
  • Readability: do the summary sentences flow in a logical way?

Use cases in the enterprise:

These are some use cases where automatic summarization can be used across the enterprise:

1. Media monitoring

The problem of information overload and “content shock” has been widely discussed. Automatic summarization presents an opportunity to condense the continuous torrent of information into smaller pieces of information.

2. Newsletters

Many weekly newsletters take the form of an introduction followed by a curated selection of relevant articles. Summarization would allow organizations to further enrich newsletters with a stream of summaries (versus a list of links), which can be a particularly convenient format in mobile.

3. Search marketing and SEO

When evaluating search queries for SEO, it is critical to have a well-rounded understanding of what your competitors are talking about in their content. This has become particularly important since Google updated its algorithm and shifted focus towards topical authority (versus keywords). Multi-document summarization can be a powerful tool to quickly analyze dozens of search results, understand shared themes and skim the most important points.

4. Internal document workflow

Large companies are constantly producing internal knowledge, which frequently gets stored and under-used in databases as unstructured data. These companies should embrace tools that let them re-use already existing knowledge. Summarization can enable analysts to quickly understand everything the company has already done in a given subject, and quickly assemble reports that incorporate different points of view.

5. Financial research

Investment banking firms spend large amounts of money acquiring information to drive their decision-making, including automated stock trading. When you are a financial analyst looking at market reports and news everyday, you will inevitably hit a wall and won’t be able to read everything. Summarization systems tailored to financial documents like earning reports and financial news can help analysts quickly derive market signals from content.

6. Legal contract analysis

Related to point 4 (internal document workflow), more specific summarization systems could be developed to analyze legal documents. In this case, a summarizer might add value by condensing a contract to the riskier clauses, or help you compare agreements.

7. Social media marketing

Companies producing long-form content, like whitepapers, e-books and blogs, might be able to leverage summarization to break down this content and make it sharable on social media sites like Twitter or Facebook. This would allow companies to further re-use existing content.

8. Question answering and bots

Personal assistants are taking over the workplace and the smart home. However, most assistants are fairly limited to very specific tasks. Large-scale summarization could become a powerful question answering technique. By collecting the most relevant documents for a particular question, a summarizer could assemble a cohesive answer in the form of a multi-document summary.

9. Video scripting

Video is becoming one of the most important marketing mediums. Besides video-focused platforms like YouTube or Vimeo, people are now sharing videos on professional networks like LinkedIn. Depending on the type of video, more or less scripting might be required. Summarization can get to be an ally when looking to produce a script that incorporates research from many sources.

10. Medical cases

With the growth of tele-health, there is a growing need to better manage medical cases, which are now fully digital. As telemedicine networks promise a more accessible and open healthcare system, technology has to make the process scalable. Summarization can be a crucial component in the tele-health supply chain when it comes to analyzing medical cases and routing these to the appropriate health professional.

11. Books and literature

Google has reportedly worked on projects that attempt to understand novels. Summarization can help consumers quickly understand what a book is about as part of their buying process.

12. Email overload

Companies like Slack were born to keep us away from constant emailing. Summarization could surface the most important content within email and let us skim emails faster.

13. E-learning and class assignments

Many teachers utilize case studies and news to frame their lectures. Summarization can help teachers more quickly update their content by producing summarized reports on their subject of interest.

14. Science and R&D

Academic papers typically include a human-made abstract that acts as a summary. However, when you are tasked with monitoring trends and innovation in a given sector, it can become overwhelming to read every abstract. Systems that can group papers and further compress abstracts can become useful for this task.

15. Patent research

Researching patents can be a tedious process. Whether you are doing market intelligence research or looking to file a new patent, a summarizer to extract the most salient claims across patents could be a time saver.

16. Meetings and video-conferencing

With the growth of tele-working, the ability to capture key ideas and content from conversations is increasingly needed. A system that could turn voice to text and generate summaries from your team meetings would be fantastic.

17. Help desk and customer support

Knowledge bases have been around for a while, and they are critical for SAAS platforms to provide customer support at scale. Still, users can sometimes feel overwhelmed when browsing help docs. Could multi-document summarization provide key points from across help articles and give the user a well rounded understanding of the issue?

18. Helping disabled people

As voice-to-text technology continues to improve, people with hearing disabilities could benefit from summarization to keep up with content in a more efficient way.

19. Programming languages

There have been multiple attempts to build AI technology that could write code and build websites by itself. It is a possibility that custom “code summarizers” will emerge to help developers get the big picture out of a new project.

20. Automated content creation

“Will robo-writers replace my job?” That’s what writers are increasingly asking themselves. If artificial intelligence is able to replace any stage of the content creation process, automatic summarization is likely going to play an important role. Related to point 3 (applications in search marketing and SEO), writing a good blog usually goes by summarizing existing sources for a given query. Summarization technology might reach a point where it can compose an entirely original article out of summarizing related search results.