TEXT SUMMARIZATION VIA DEEP LEARNING

Abstract

This knowledge these days is stored in various formats in huge repositories mostly in the form of documents, sheets, photos, videos. One finds it difficult to comprehend this whole lot of information. There by, here comes the need of text summarization [2]. Text summarization is a process of extracting the context of a large document and summarize it into a smaller paragraph or a few sentences. Text summarization plays a vital role in saving time in our day to day life. It is also used in many bigger project implementations of classification of documents or in search engines [8]. Text Summarization has become an important and timely tool for assisting and interpreting text information. It is generally distinguished into: Extractive and Abstractive. The first method directly chooses and outputs the relevant sentences in the original document; on the other hand, the latter rewrites the original document into summary using Natural Language Processing (NLP) techniques. From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation. This paper gives a brief survey of the distinct attempts undertaken in the field of abstractive summarization [5].