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June 21, 2024

Text Summarization Using NLP: Techniques and Use Cases


Edwin Lisowski

CSO & Co-Founder

Reading time:

14 minutes

The volume of written information has increased exponentially over the years thanks to the rise of the internet and the increasing popularity of social media. As a result, the ability to efficiently break down large chunks of text into smaller and easily understandable sentences or paragraphs has become crucial for many organizations.

Unfortunately, it’s quite tedious and time-consuming to manually summarize lengthy texts. You also risk leaving out important details when sifting through sensitive documents. This is where text summarization comes in handy.

Below is an in-depth review of what text summarization is, how it works, various techniques used, and its most common use cases.


What is text summarization for NLP?

Text summarization is a subset of Natural Language Processing (NLP) that uses advanced algorithms and machine learning models to analyze and break down lengthy texts into smaller digestible paragraphs or sentences. This procedure extracts the most valuable information from a particular text without altering its original meaning. It can be applied in a wide variety of domains, including academia, business, and news.

Text summarization not only reduces the time and effort required to read and understand lengthy texts, but it also ensures the accuracy and completeness of a summary. It is often useful when the text has many raw facts that can be filtered from it. This makes NLP text summarization a valuable tool when it comes to summarizing technical documents, financial materials, and sensitive legal texts.

NLP text summarization techniques

Here are the most common types of NLP text summarization techniques:

Input-based NLP text summarization

One of the most common ways of classifying NLP text summarization is based on the input. In this case, text summarization can be from a single document or multiple documents. Single-document summarization basically refers to an NLP text summarization technique whereby the input is from a single document. Most of the early text summarization systems commonly dealt with single-document summarization. [1]

On the other hand, multi-document summarization involves an input of multiple documents. Multi-document NLP text summarization is a bit more challenging compared to single-document summarization because it requires the ability to analyze and understand the underlying relationship between multiple documents/texts and extract a summary of the essential information from them.

Output-based NLP text summarization

Another great way of classifying NLP summarization is on the basis of the output or format of the final summary. There are two types of output-based text summarization, namely extractive and abstractive text summarization.

Extractive text summarization

Extraction summarization is an NLP summarization technique that involves selecting and isolating essential information from a pre-existing text or document and compressing the information to form a summary. You can think of this summarization technique as a highlighter that allows you to see the main points in a given text. [2]

Extractive text summarization

In machine learning, extractive summarization is based on features such as word frequency, sentence centrality, text ranking, and sentence length. This procedure also relies on machine learning models and graph-based algorithms to weigh key sections of a given text and use the results to generate a summary or shortened form of the original text. Today, most NLP text summarization techniques are extractive in nature.

Abstractive text summarization

Abstractive text summarization is an NLP summarization technique that uses Natural Language Generation (NLG) to generate a summary that is a new and unique representation of the original text. [3] This technique relies on advanced machine learning and natural language processing to understand the meaning and context of the original text and then generates a summary that captures the main ideas.

Basically, abstractive text summarization is quite similar to how humans summarize texts. In our minds, we usually create a phonological representation of the text and pick terms from our daily vocabulary that align with the semantics of the original text; then we generate a meaningful summary. That said, it’s possible that a summary generated using abstract text summarization contains phrases that aren’t present in the original text.

abstractive text summarization

Although abstract text summarization is more appealing and efficient than extractive summarization, it’s more complex. This is because abstractive summarization requires a deeper understanding of the content and the ability to generate a new and unique summary without changing the meaning and structure of the original text. Additionally, the algorithms required in this technique tend to be more difficult to train and build since they need huge volumes of data.

Purpose-based NLP text summarization

On the basis of the purpose or goal of the summary, NLP text summarization can be classified into the following:

text summarization using nlp

Generic summarization

Generic text summarization is a summarization technique that provides an overview of the main points or ideas in a particular text without making any assumptions about the content. This technique simply condenses or summarizes the original text and treats all inputs as homogenous. Most of the work done in text summarization revolves around generic summarization.

Domain-specific summarization

This is a type of summarization technique that uses domain-specific knowledge to generate a summary that is tailored to the domain of the original text. For example, machine learning models can be trained using medical science terminology so that they can generate more accurate summaries of texts and documents in this particular field.

In most cases, domain-specific summarization techniques are used to summarize legal documents, technical reports, and other specialized texts.

Query-based summarization

The primary focus of this type of NLP text summarization is to generate a summary that answers specific natural language questions about a particular text. This is a similar technique to the one used to generate search results on popular search engines such as Google and Bing. [4]

Every time you type a question on a search engine like Google, it returns a selection of websites or articles that answer your question. The search results usually contain a summary of an article that directly answers your question or is relevant to what you’re searching for.

Dive into the possibilities of our NLP solutions and unlock the potential of your business strategy through cutting-edge text summarization technology.

NLP text summarization use cases

NLP text summarization has several use cases and applications across a wide variety of industries. These use cases include:

Financial research

Overall, financial and investment decisions in organizations require a great deal of investigation and summarization of huge volumes of financial reports and statistics. Individual investors and organizations can use NLP text summarization to identify recurring trends from vast amounts of financial information. This helps stakeholders, financial analysts, investors, and financial advisors to make more informed decisions.

Additionally, NLP text summarization can be used to formulate valuable hypotheses about various financial markets worldwide. This helps financial advisors and researchers to develop better trading strategies that can help companies increase their profits and avoid losses in the long run.

In some cases, NLP text summarization can also be used to generate concise and informative summaries of financial research findings. This way, financial researchers can easily communicate their findings about financial markets and individual securities to other researchers, investors, and the general public.

Media monitoring

Assume you want to know the current state of a given industry from huge amounts of publications and media. However, you don’t have the time to scan through every headline on these publications, let alone read every document related to the industry.

In such cases, text summarization can be used to break down these publications and media into concise and meaningful summaries. With the help of summaries, you’ll be able to stay informed about the current events of a given industry.

Chapters for youtube videos and podcasts

Troubleshooting YouTube videos should always go directly to the point. Viewers are more likely to be frustrated if they have to waste time watching a long YouTube video that doesn’t quickly provide a solution to their problem. The same goes for podcasts. Some listeners want to get to the best bits as soon as possible.

If you create YouTube videos and podcasts, NLP text summarization can be a valuable tool when it comes to generating chapters or sections for your content. This makes it a lot easier for your viewers and listeners to find the specific sections of your content they’re interested in. Breaking up your YouTube videos and podcasts into sections also makes it easier for your viewers and listeners to digest the information you’re trying to present and follow your train of thought.

Email thread summarization

If you run a business and usually receive many emails from clients/customers, you can use a text summarization tool to quickly identify the main points from various conversations. This is particularly helpful, especially if you’re usually busy and want to understand the content in your email inbox without actually reading through hundreds or even thousands of emails.

With the help of a concise and informative summary, you’ll know which particular emails require immediate action and then act accordingly. This will help you stay on top of customer feedback and keep your business running smoothly.


As a business owner, knowing your customers’ needs can help you develop products and services that align with their needs. One of the best ways to gain insights into your customers’ needs is through search queries on SEO search engine queries. Aligning your meta descriptions with customers’ search queries will help your business website rank higher in search engine results. [5]

Thanks to multi-document summarization, you can analyze different search engine results and understand shared themes. This way, you can leverage keyword targeting and optimize your web content to achieve top listings on popular search engines like Google, Yahoo, and Bing.

Customer feedback and reviews

One of the best applications of text summarization is in generating clear summaries of customer feedback or reviews regarding various products. With the help of NLP text summarization, businesses can easily identify the most common issues and topics customers are voicing through their feedback and reviews. Businesses can then use this information to improve the quality of their products and services. In the long run, this helps improve customer satisfaction and build brand loyalty.

Text summarization can also be used to generate valuable insights into customer needs and desires. Businesses can use these insights to develop new products that align with customers’ current and future needs. Such products will help businesses generate more revenue and increase their profitability.

Legal document analysis

Complex legal jargon and lengthy documents usually make it difficult to understand legal contracts and agreements. With the help of text summarization, lawyers, paralegals, and other legal professionals can easily understand the main points of legal documents without having to spend hours reading the entire thing.

Text summarization also helps legal professionals to identify the most important parts of legal documents. This way, they can ensure their clients understand and comply with the respective statute of limitations, applicable laws, and regulations. Most importantly, text summarization makes it easier for lawyers and other legal professionals to compare different legal documents and understand the implications of different legal provisions.

ContextClue – a powerful AI-driven AI knowledge assistant

The rise of Generative AI, particularly Large Language Models (LLMs), has enhanced the capabilities of “traditional” text summarization tools. However, despite the hype, incorporating these models into corporate environments remains challenging.

A major obstacle to deploying LLMs in production is the quality evaluation process. Alongside concerns about data security and the opaque nature of AI’s decision-making processes, these concerns lead businesses to question whether these models can meet high standards of accuracy and reliability.

Nevertheless, this challenge presents an opportunity for AI vendors to customize, tailor, and enhance existing LLM models to align with specific business needs and accuracy standards. ContextClue, designed and developed by Addepto, addresses these concerns effectively.

ContextClue has developed a comprehensive Retrieval-Augmented Generation (RAG) application framework designed to tackle the quality evaluation problem of Large Language Models (LLMs).

This framework employs a combination of algorithms, metrics, LLMs, and other complex logic to ensure that the responses generated by LLMs are accurate and reliable based on the company’s knowledge base.

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Wrapping up

As shown in this guide, there are plenty of NLP text summarization techniques to choose from. Generally, the ideal text summarization technique mainly depends on your specific needs or goals as well as the available resources and expertise. In some cases, it may be necessary to combine different summarization techniques to achieve the desired results.

As technology continues to advance, the field of text summarization will continue to grow, and better NLP text summarization techniques will be developed. With more efficient summarization techniques, the accuracy, conciseness, completeness, and informativeness of summaries will also improve.

Text Summarization Using NLP: Techniques and Use Cases – FAQ

What is text summarization in NLP?

Text summarization is a subset of Natural Language Processing (NLP) that uses advanced algorithms and machine learning models to analyze and condense lengthy texts into smaller, digestible paragraphs or sentences. This process extracts the most valuable information from a text without altering its original meaning, making it useful in various domains such as academia, business, and news.

Why is text summarization important?

Text summarization reduces the time and effort required to read and understand lengthy texts, ensuring the accuracy and completeness of a summary. It is particularly useful for summarizing technical documents, financial materials, and sensitive legal texts, where important details can be easily overlooked.

What are the common NLP text summarization techniques?

Input-based NLP Text Summarization:

  • Single-Document Summarization: Involves summarizing content from a single document.
  • Multi-Document Summarization: Involves summarizing content from multiple documents, which is more complex due to the need to understand relationships between different texts.

Output-based NLP Text Summarization:

  • Extractive Summarization: Selects and isolates essential information from the original text to create a summary.
  • Abstractive Summarization: Generates a new, unique summary that captures the main ideas of the original text using Natural Language Generation (NLG).

Purpose-based NLP Text Summarization:

  • Generic Summarization: Provides an overview of the main points in a text without specific assumptions about the content.
  • Domain-Specific Summarization: Uses domain-specific knowledge to generate tailored summaries.
  • Query-Based Summarization: Generates summaries that answer specific questions about the text.

What are some common use cases for NLP text summarization?

  • Financial Research: Summarizing financial reports and statistics to identify trends and inform investment decisions.
  • Media Monitoring: Breaking down publications into concise summaries to stay informed about industry events.
  • Chapters for YouTube Videos and Podcasts: Creating chapters or sections for content to enhance viewer and listener experience.
  • Email Thread Summarization: Summarizing email conversations to quickly identify key points.
  • SEO: Analyzing search engine results to optimize web content for higher rankings.
  • Customer Feedback and Reviews: Summarizing feedback to identify common issues and improve products.
  • Legal Document Analysis: Summarizing legal documents to understand key points without reading the entire text.

What challenges are there in deploying LLMs for text summarization in corporate environments?

Deploying Large Language Models (LLMs) in corporate environments faces several challenges:

  • Quality Evaluation: Ensuring the accuracy and reliability of summaries generated by LLMs.
  • Data Security: Protecting sensitive information during processing.
  • Decision-Making: Addressing concerns about the transparency of AI’s decision-making processes.

How can these challenges be addressed?

AI vendors can customize, tailor, and enhance existing LLM models to meet specific business needs and accuracy standards. ContextClue, developed by Addepto, addresses these concerns by using a comprehensive Retrieval-Augmented Generation (RAG) application framework. This framework combines algorithms, metrics, LLMs, and other complex logic to ensure accurate and reliable responses based on the company’s knowledge base.

What advancements can we expect in text summarization technology?

As technology advances, we can expect more efficient NLP text summarization techniques that improve the accuracy, conciseness, completeness, and informativeness of summaries. This will enhance the ability to summarize complex documents across various industries, leading to better decision-making and improved productivity.

This article is an updated version of the publication from Nov 14, 2023.


[1] Single Document Summarization. URL: . Accessed on November 13, 2023
[2] Auto highlighter: Extractive Text Summarization wit Sequence-to-Sequence Model. URL: Accessed on November 13, 2023
[3] What is NLG? URL: Accessed on November 13, 2023
[4] How do Search Engines Work? URL: Accessed on November 13, 2023
[5] Meta Description. URL: Accessed on November 13, 2023


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