A Constraints Driven PSO Based Approach for Text Summarization

Authors

  • Shrabanti Mandal Department of Computer Science & Applications, Dr. Harisingh Gour Central University, Sagar 470003
  • Girish Kumar Singh Department of Computer Science & Applications, Dr. Harisingh Gour Central University, Sagar 470003
  • Anita Pal Department of Mathematics, National Institute of Technology, Durgapur, West Bengal

DOI:

https://doi.org/10.26713/jims.v10i4.891

Keywords:

Text summarization, Particle swarm optimization, K-means, Fitness function, Cosine measure and ROUGE

Abstract

In the present scenario we are living in a digital media and virtual world. To conveniently communicate in digital world electronic data have to gradually increase. So it is a serious challenge to manage the huge digital and electronic resources efficiently and accurately. One of the important solutions of the above problem is text summarization i.e. an application of text mining. Representing the gist of a text document is called summary. A rich summary always covers the maximum coverage, high level of diversity and with user defined size. This paper proposes an approach for summarizing the text documents by extractive way using Particle Swarm Optimization (PSO) that is known as population based stochastic optimization technique and it has many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The huge volume and dimensions of terms have managed by the concepts of term document matrix followed by K-Means clustering with PSO for acquiring optimal number of concepts clusters. Then apply constraint-driven concept for selecting the best one. These key concepts were used to identify the significant gist in documents for text summarization.

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Published

2018-12-31
CITATION

How to Cite

Mandal, S., Singh, G. K., & Pal, A. (2018). A Constraints Driven PSO Based Approach for Text Summarization. Journal of Informatics and Mathematical Sciences, 10(4), 703–714. https://doi.org/10.26713/jims.v10i4.891

Issue

Vol. 10 No. 4 (2018)

Section

Research Articles

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