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Extractive multi-document text summarization using dolphin swarm optimization approach

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Abstract

Nowadays, extracting the desired information from internet source is a challenging task because of a large amount of information available on the internet. So, we propose a new extractive based approach for multi-document text summarization to extract useful information from multi-document. Initially, the redundant contents in the document create a single text file from the multiple text file document. The content coverage and non-redundancy features are achieved by Word Mover Distance (WMD) and Modified Normalized Google Distance (M-NGD) (WM) Hybrid Weight Method based similarity approaches. For feature weight optimization, we use the Dolphin swarm optimization (DSO) which is a metaheuristic approach. The proposed approach is tested under python with multiling 2013 dataset and the performances have been evaluated with ROUGE and AutoSummENG metrics. The investigational outcomes show that the proposed technique works well and very much effective for multi-document text summarization.

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Authors and Affiliations

  1. Research Scholar, Dr. APJ Abdul Kalam Technical University , Lucknow, India

    Atul Kumar Srivastava

  2. Amity University, Sector 125, Noida, India

    Atul Kumar Srivastava

  3. Department of Computer Science and Engineering, JSS Academy of Technical Education, AKTU, Noida, India

    Dhiraj Pandey

  4. Department of Computer Science and Engineering, University of Petroleum & Energy Studies, Dehradun, India

    Alok Agarwal

Authors
  1. Atul Kumar Srivastava
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  2. Dhiraj Pandey
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  3. Alok Agarwal
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All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

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Correspondence to Atul Kumar Srivastava.

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Srivastava, A.K., Pandey, D. & Agarwal, A. Extractive multi-document text summarization using dolphin swarm optimization approach. Multimed Tools Appl 80, 11273–11290 (2021). https://doi.org/10.1007/s11042-020-10176-1

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