Automatic Text Summarization using Natural Language Processing

Abstract

Automatic text summarization is basically summarizing of the given paragraph using natural language processing and machine learning. There has been an explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods. Two types will be used i.e.-extractive approach and abstractive approach. The basic idea behind summarization is finding the subset of the data which contains the information of all the set. There is a great need to reduce unnecessary data. It is very difficult to summarize the document manually so there is the great need of automatic methods. Approaches have been proposed inspired by the application of deep learning methods for automatic machine translation, specifically by framing the problem of text summarization as a sequence-to-sequence learning problem.