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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Particle swarm optimization model for Hindi text summarization

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pp. 839–850Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1609XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1609
Pages:
839–850

Abstract

Text Summarization is one of the techniques that shorten the original text without vanishing its information as well as meaning. A lot of algorithms exist for text summarization. Two approaches namely Abstractive Text Summarization and Extractive Text Summarization are used for this purpose. In Abstractive text summarization, the entire document is regenerated using a few lines. Whereas in Extractive Text Summarization sentences are filtered based on some ranks assigned to them by a specific algorithm. A lot of work has already been done in languages like English, Chinese etc. In this paper, the authors propose the summarization of Hindi text using the Particle Swarm Optimization model. Initially, the text in the Hindi language is summarized using a ranking-based technique then PSO (Particle Swarm Optimization) is applied to have an optimized summary of the text. One of the ranking-based techniques i.e. TF-IDF is introduced. Implementation of the proposed Systems is initially discussed in five steps- preprocessing, feature extraction, ranks generation, post-processing and optimized summarization using PSO. At the end, results are shown in terms of an optimized summary of text in a specific language. This system can be implemented in any standard language, but Hindi is selected for practical implementation because very few research work is done in the Hindi Language.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

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