Evolutionary Algorithms for Extractive Automatic Text Summarization

Abstract

Due to the exponential growth of documents on internet, users want all the relevant data at one place without any hassle. This led to the growth of Automatic Text Summarization. For extractive text summarization in which representative sentences from the document itself are selected as summary, various statistical, knowledge based and discourse based methods are proposed by researchers. The goal of this paper is to give a survey on the important techniques and methodologies that are employed using Genetic Algorithms in Automatic Text Summarization. This paper gives a review of the growth and improvement in the techniques of Automatic Text Summarization on implementing Evolutionary Algorithms techniques. We propose a broad set of features that considers additional features in the fitness function.