How NLP Helps Summarizing Tools to Create Engaging Summaries

How NLP Helps Summarizing Tools to Create Engaging Summaries

Natural Language Processing (NLP) is a type of AI that has been around for a while. So, how does it help the summarize tools?

A Summarizing tool depends on a variety of AI elements. One of the chief ingredients in any summarizer is NLP (natural language processing). 

So, what exactly is NLP? How does it work with a Summarizing tool to create engaging summaries? Let’s find out. 

What is NLP?

Natural Language Processing, usually abbreviated to NLP, is a subfield of computer science. This branch of Artificial intelligence helps computers understand human languages. NLP has been around for about 60 years, but it was not until recently that it became more popular. 

Since it is the process of analyzing and understanding text or speech using computational methods, it’s highly employed in many tools. However, NLP is used in many different ways, such as in paraphrasing tools, summarizers, plagiarism checkers, grammar correctors, etc. 

It helps to better understand language so machines can analyze and understand it. The use of NLP in everyday life has increased exponentially in the last decade or so due to its integration with other technologies like machine learning and speech recognition.

How Does NLP Help Summarizing Tools With Creating Engaging Summaries?

How NLP Helps Summarizing Tools to Create Engaging Summaries

NLP is the key to any writing tool, so there’s no wonder a summarizing tool would use this AI branch quite profusely. But, to understand how it helps create engaging summaries, we have to look at its key advantages.

Therefore, we will explore the four most effective ways NLP drives a summarizing tool to create engaging summaries. Here they are:

1. It Can Create an Extractive Summary

Summaries come in two main types. The first is extractive, and the other is abstractive. As you can judge by the name, the first one deals with extracting keywords from within any content. Whereas the latter writes a summary by abstracting the concept.

So, a summarizer can usually create an extractive summary, using frequently appearing words to create a shorter text version. Whereas an abstractive summary is the paraphrased and shorter version of the text. 

So, to understand both, we’ll look at the abstractive summary written manually and an extractive summary written with the help of an NLP summarizer.

Main Content:

The man brought his vulnerable car to work. However, after the car broke down, he had to call mechanics. This took another 2 hours, which made him late for his son’s birthday.

Abstractive Summary Example:

Man had to call the mechanic after bringing his susceptible car to work. The fix took two hours, making him late for his son’s birthday.

Extractive Summary through NLP:

Man brought vulnerable car to work. Car broke down, man was late for his son’s birthday.

As you can see, the Extractive summary isn’t necessarily rich with information; but it does generalize the information. Whereas Abstractive summary focuses more on the concept than specific words. 

This allows NLP to help the summarizing tool capture any content’s essence. Hence, writing engaging summaries that embody the main idea.  

2. Abstractive Summary-Making Approach

NLP helps summarizing tools to make the abstractive summary of any content. Abstractive summary means that the tool will not take the exact same lines from the original text to paste them in the summary instead, it will read and understand the content to write the whole summary on its own.

It saves you from the repetition of text in the content because repetition may sound weird to the readers. Only taking the idea from the original text to write a unique summary is the best approach that readers will like.

So, how does NLP help a summarizer do that? Here’s how:

  • It analyzes the key concepts mentioned in the main content;
  • It separates concepts and ideas from fluff/secondary text;
  • It comprehends the main topic and sub-topics;
  • Then write the summary on its own.

3. Covers All the Important Information 

Providing information is the key aspect of any summary. If you were to describe the basic pillar of a summary, it would be “just the right amount of information.” Because you need the target reader to peruse the main content—the summary is just a way to attract them towards it.

That’s why it’s imperative for a summarizing tool to capture the key information, concepts, and ideas presented in the main text. Here are three points to help you understand how NLP helps cover all the important information:

  • NLP analyzes the input and understands the key subject/topic;
  • Helps the summarizer form a phonological understanding of the content’s general concept;
  • Picks the right vocabulary to describe particular information—without revealing it all.

Hence, when you use an NLP summarizing tool, the summary you get is the extraction of key components within the text. And it’s all made possible with the help of NLP, which analyzes and picks the right aspects of content to use in summary. 

Thus, creating an engaging summary that thoroughly covers all the key areas of information within the text. 

4. Allow Summarizing Tool To Properly Condense The Text

As mentioned before, NLP helps computers understand human language. So, when the tool reads the text input, it needs to capture the key ideas discussed in the text. On the other hand, it also needs to condense that text into 20%, 33%, or 50% summaries.

Now, think about it, if you are to write a 20% summary of a 1000-word blog, then the information needs to abbreviate into 200 words. So, all that is only made possible with the help of NLP. 

It helps the AI recognize key concepts and then create an abstract summary. Thus, the AI can condense the text thoroughly when the user chooses a particular summary length.


These are the key aspects of NLP and how they help a summarizing tool. It’s important to remember that NLP is basically the eyes and ears of AI—since it reads and comprehends the text. So, it’s the implementation of summarizing tools is something that depends entirely on these four aspects. 

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