Automatic Text Summarization

Most of us did many exercises in elementary school to improve our ability to summarise a text. The summarization process is essential for efficiently understanding the most important parts of a text, assisting our brain in memorizing the text, and providing a foundation for linking different topics.

Our digital world is more content-rich than ever, and the need to summarise has never been greater. However, a human being cannot keep up with the rate at which new content is created. We require an automated system to extract the most important issue from the text.

This is especially true when we consider using a similar tool in a business setting: we could increase the efficiency of a specific process and, in some cases, even automate some simple tasks.

What is the point of departure? An excellent text summarization program. We will delve deeper into the theme and its main applications in this article.

Automatic Text Summarization: state of the art 

The process of computationally shortening a text to create a summary that contains the most relevant parts of the original content is known as automatic text summarization.

Obviously, the critical point is to train the machine to understand the grammar and semantics of the text in order to re-build its meaning and re-shape it in a reduced form, not to reduce the number of words or letters.

Automatic text summarization is now considered to be part of a larger field known as Natural Language Processing (NLP).

Natural Language Processing is a subfield of Artificial Intelligence that aims to train machines to process natural language and sometimes re-use natural language to answer questions, as many of you are aware (for example, we can think of chatbots or voice assistants).

There are two main techniques to summarize a text in a sensible way:

  • Extraction-based summarization: The algorithm in this case extracts the key phrases and combines them to form a new phrase.
    This method is less complicated than the following, but the summary may be strange and grammatically incorrect at times.
  • Abstraction-based summarization: using this technique, the algorithm creates new phrases, relaying the most useful information from the original text. It is easier to understand that this method is more similar to what humans do and abstraction, indeed, works better than extraction. Obviously, it is more difficult to develop a system based on abstraction.

 

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