AI Text Summarizer: Abstractive and Extractive Summarization Explained

With the advancements in AI, many tools have become more intelligent. The same is the case with text summarizers. Most of them have started using AI to produce more effective and accurate results.

But that is not the only thing that enables them to come up with good results. They also use Natural Language Processing (NLP) and Machine Learning (ML) for that. If you want to learn more about the inner workings of these processes, keep reading as we’ll explain everything. But before we do that, let us tell you what an AI text summarizer really is and how it employs abstractive and extractive summarization techniques.

What is an AI Text Summarizer?

An AI text summarizer can generate summaries for users by using artificial intelligence. The tool does that by understanding the whole text first, then collecting its main points and ideas to effectively join them together so that the user can get the crux of the original text from the generated one.

The text summarizer tool generates summaries without missing out on any of the main points that might alter the meaning or not convey the entire message.

Apart from this, it can also exclude anything irrelevant and can cause issues of clarity. This ensures that the ai generated summary has a high level of readability and clarity.

Tools like these are easy to use and have a nice and easy-to-understand UI, so the users understand what to do without having to read a manual to use it. Here’s what a basic AI text summarization usually looks like.

How NLP and ML work in an AI Text Summarizer

To make it easier for you to understand, we will divide this discussion section into two parts and explain how each AI model works in a summarizer one by one.

NLP in Abstractive and Extractive Summarization

NLP is the AI model that is used in a program to understand human language. Since machines can’t understand the language of humans, NLP is used to convert this language to a binary code so the tool can effectively understand the text given to it.

It does that by analyzing the syntax of the text. NLP enables the summarizer to analyze each sentence and word one by one so it can accurately determine what the text is about and what are its key points.

NLP also enables the extractive summarizing tool to understand the context of the text given to it by the user. It examines the words' relation and hierarchy in a text to understand its context. Doing this helps the tool generate an appropriate summary for the given content since not every summary has the same structure.

For example, an executive summary is a bit different in its structure than an essay summary. Understanding the context of the text using NLP helps the tool determine what type of text is given to it and what’s the most appropriate summary for it.

All in all, NLP enables the AI summarizer to effectively understand a text to carry out the next functions better.

ML in Abstractive and Extractive Summarization

Once the text is comprehended by the summarizer using NLP, it is time for ML to take the reins. It is the AI model that is responsible for generating the actual summary.

There are mainly two types of summaries: extractive and abstractive.

Extractive summarization techniques select and combine sentences from the text, while abstractive summarization techniques generate new sentences that maintain the original essence.

ML can generate any of the two summarizing techniques based on what would be the most accurate one. It figures that out on its own. Now, you might be wondering, how can it do that on its own?

Well, the answer is pretty simple. ML models are trained on a huge database of text. It accesses that database and makes the summarizing tool generate the most accurate result.

ML has the ability to learn from each input that the tool receives and each output it generates. This keeps increasing the database on which it operates and improving the tool’s accuracy over time with each input and output.

In simple terms, ML is a self-learning AI algorithm used for generating summaries. It is also integrated into an AI summary generator to improve its performance and results over time continuously.

Conclusion

A text summarizer is an AI tool for generating summaries. It does that by using AI models like machine learning and natural language processing. In this article, we’ve discussed the role of both these models in a summarizing tool in detail, as well as the methods for abstractive and extractive summarization. Learn how natural language processing is used to create NLP chatbots, if you enjoyed this article.