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Particle Swarm Optimization for Punjabi Text Summarization

Particle Swarm Optimization for Punjabi Text Summarization

Arti Jain, Divakar Yadav, Anuja Arora
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 17
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781799861225|DOI: 10.4018/IJORIS.20210701.oa1
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MLA

Jain, Arti, et al. "Particle Swarm Optimization for Punjabi Text Summarization." IJORIS vol.12, no.3 2021: pp.1-17. http://doi.org/10.4018/IJORIS.20210701.oa1

APA

Jain, A., Yadav, D., & Arora, A. (2021). Particle Swarm Optimization for Punjabi Text Summarization. International Journal of Operations Research and Information Systems (IJORIS), 12(3), 1-17. http://doi.org/10.4018/IJORIS.20210701.oa1

Chicago

Jain, Arti, Divakar Yadav, and Anuja Arora. "Particle Swarm Optimization for Punjabi Text Summarization," International Journal of Operations Research and Information Systems (IJORIS) 12, no.3: 1-17. http://doi.org/10.4018/IJORIS.20210701.oa1

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Abstract

Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for parallel corpus with precision, recall, and F-measure as 0.7836, 0.7957, and 0.7896, respectively.