CSMDSE-Cuckoo Search Based Multi Document Summary Extractor: Cuckoo Search Based Summary Extractor

Rasmita Rautray, Rakesh Chandra Balabantaray, Rasmita Dash and Rajashree Dash
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Rasmita Rautray: Department of Computer Science and Engineering, Siksha ‘O' Anusandhan, Deemed to be University, Bhubaneswar-751030, Odisha, India
Rakesh Chandra Balabantaray: Department of Computer Science, IIIT, Bhubaneswar, Odisha, India
Rasmita Dash: Department of Computer Science and Engineering, Siksha ‘O' Anusandhan, Deemed to be University, Bhubaneswar-751030, Odisha, India
Rajashree Dash: Department of Computer Science and Engineering, Siksha ‘O' Anusandhan, Deemed to be University, Bhubaneswar-751030, Odisha, India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2019, vol. 13, issue 4, 56-70

Abstract: In the current scenario, managing of a useful web of information has become a challenging issue due to a large amount of information related to many fields is online. The summarization of text is considered as one of the solutions to extract pertinent text from vast documents. Hence, a novel Cuckoo Search-based multi document summary extractor (CSMDSE) is presented to handle the multi-document summarization (MDS) problem. The proposed CSMDSE is assimilating with few other swarm-based summary extractors, such as Cat Swarm Optimization based Extractor (CSOE), Particle Swarm Optimization based Extractor (PSOE), Improved Particle Swarm Optimization based Extractor (IPSOE) and Ant Colony Optimization based Extractor (ACOE). Finally, a simulation of CSMDSE is compared with other techniques with respect to the traditional benchmark datasets for summarization problem. The experimental analysis clearly indicates CSMDSE has good performance than the other summary extractors discussed in this study.

Date: 2019
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