A Review on Neural network based Abstractive Text Summarization models

Abstract

Text summarization is the process to create short and concise summaries from the text document with the aim of provide most important or salient content in a condensed form from the source document. As the information is overloaded on the web, text summarization becomes necessary. Extractive and abstractive are two broad approaches of text summarization. Extractive method assembles summaries from the document, which directly take a sentence or phrase from the original document while abstractive method may generate new words to make a summary. This paper gives the comparison of various neural network based abstractive text summarization models and also discuss the types of summarization based on categories and different approaches of abstractive text summarization. At last, comparison of these models are also given.