Improved text summarization of news articles using ga-hc and pso-hc

Muhammad Mohsin, Shazad Latif, Muhammad Haneef, Usman Tariq, Muhammad Attique Khan, Sefedine Kadry, Hwan Seung Yong, Jung In Choi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

Original languageEnglish
Article number10511
JournalApplied Sciences (Switzerland)
Volume11
Issue number22
DOIs
StatePublished - 1 Nov 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Agglomerative clustering
  • Automatic Text Summarization (ATS)
  • Extracted summary
  • Genetic algorithm
  • Hierarchical Clustering Technique (HCT)
  • Single Document Summarization

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