Efficient Text Summarization using Natural Language Processing

Abstract

In recent years, a huge volume of textual data has been released through social media and various other domains that can be extracted and analyzed to reproduce useful information. The underlying information from the data should be retrieved and summarized within a specific period for effective analysis. Text summarization is the process of condensing a text document into a briefer version while retaining its key information and meaning and understanding the essence of a document. This process enables the user to save time. It has a wide range of applications in various domains such as news articles, scientific papers, legal documents, social media, and more.There are two ways to implement Text summarization: Extractive and Abstractive Summarization. Extractive summarization involves generating summaries from the original text but cannot frame new sentences. Another approach is abstractive summarization, which involves generating new sentences that capture the essence of the original text. Text summarization can significantly reduce the time and effort required to extract important information from large volumes of text. Experimental data can be used to validate the system's effectiveness and fine-tune the various modules involved in the text summarization process.This paper explores the implementation of different text summarization approaches to summarize the text data collected from different sources and analyze their performance using the ROUGE score. The abstractive text summarization method is more effective than the extractive summarization for the given set of input text chosen.