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Cat swarm optimization based evolutionary framework for multi document summarization

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  • Rautray, Rasmita
  • Balabantaray, Rakesh Chandra

Abstract

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it 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 Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, 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 the study.

Suggested Citation

  • Rautray, Rasmita & Balabantaray, Rakesh Chandra, 2017. "Cat swarm optimization based evolutionary framework for multi document summarization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 174-186.
    • Handle: RePEc:eee:phsmap:v:477:y:2017:i:c:p:174-186
    DOI: 10.1016/j.physa.2017.02.056
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    References listed on IDEAS

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    1. Rasmita Rautray & Rakesh Chandra Balabantaray & Anisha Bhardwaj, 2015. "Document Summarization Using Sentence Features," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 5(1), pages 36-47, January.
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    Cited by:

    1. Altay, Elif Varol & Alatas, Bilal, 2020. "Randomness as source for inspiring solution search methods: Music based approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Chandrakala Arya & Manoj Diwakar & Prabhishek Singh & Vijendra Singh & Seifedine Kadry & Jungeun Kim, 2023. "Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    3. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.

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