An evolutionary framework for multi document summarization using Cuckoo search approach: MDSCSA

Abstract

In today's scenario the rate of growth of information is expanding exponentially in the World Wide Web. As a result, extracting valid and useful information from a huge data has become a challenging issue. Recently text summarization is recognized as one of the solution to extract relevant information from large documents. Based on number of documents considered for summarization, the summarization task is categorized as single document or multi-document summarization. Rather than single document, multi-document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cuckoo search based multi-document summarizer (MDSCSA) is proposed to address the problem of multi-document summarization. The proposed MDSCSA is also compared with two other nature inspired based summarization techniques such as Particle Swarm Optimization based summarization (PSOS) and Cat Swarm Optimization based summarization (CSOS). With respect to the benchmark dataset Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of ROUGE score, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in this study. Keywords: Text summarizer, Multi-document summarization, Extractive summary, Cuckoo search