Text Summarization of Natural Language Processing

Text summarization is the process of creating a shorter version of a text document while preserving its most important information. This can be done manually or automatically using natural language processing (NLP) techniques.

There are two main approaches to text summarization:

1. extractive 
2. abstractive.

1. Extractive 

Extractive summarization involves identifying the most important sentences in a text document and then creating a summary that only includes those sentences. This can be done using a variety of techniques, such as keyword extraction, sentence scoring, and graph-based summarization.

2.Abstractive

Abstractive summarization involves understanding the meaning of a text document and then generating a summary that conveys that meaning in a new way. This is a more challenging task than extractive summarization, but it can produce summaries that are more informative and engaging.

Text summarization is a valuable tool for a variety of applications, such as news aggregation, research, and education. It can help people to quickly and easily understand the key points of a long text document, without having to read the entire document.