Text Summarization Using Deep Learning

Abstract

In the big data era, there has been an explosion in the amount of text data from a variety of sources. This volume of text is an inestimable source of information and knowledge which needs to be effectively summarized to be useful. There are plenty of text material available on the internet. Earlier traditional approaches for extractive text summarization have been heavily dependent on human engineered features. However, it is a laborious and tedious task. In this paper, a data-driven approach has been used to generate extractive summaries using deep learning. So there is a problem of searching for relevant documents from the number of documents available, and absorbing relevant information from it. Thus, the need for a solution emerges, that transforms this vast raw information into useful data which a human brain can understand. One such common technique in research that helps in dealing with enormous data is text summarization. We must first comprehend what a summary is before moving on to text summarization. A summary is a text created from one or more texts that delivers key information from the source material in a condensed style. The most significant benefit of adopting a summary is that it cuts down on reading time. Text summarization is the process of extracting the most important meaningful information from a document or set of related documents and compressing it into a shorter version while retaining its overall meanings. Automatic summary is a well-known method for refining a document's important points. It works by providing a reduced version of the text that preserves significant information. There are two types of text summarization methods: Extractive and Abstractive.