Extractive text summarization of arabic multi-document using fuzzy C-means and Latent Dirichlet Allocation

Ahmad T. Al-Taani () and Sami H. Al-Sayadi
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Ahmad T. Al-Taani: Yarmouk University
Sami H. Al-Sayadi: Yarmouk University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 2, No 11, 713-726

Abstract: Abstract In this research, we investigated the performance of the combination of fuzzy c-means and latent Dirichlet allocation algorithms for Arabic multi-document summarization. The summary should include the most essential sentences from multi-documents with the same topic. The TAC-2011 corpus is used for experiments, first, the documents in the corpus are clustered using fuzzy c-means algorithm. The aim of the clustering process here is to classify the documents according to their topics, e.g., economic, politic, sport, etc. The results are compared against some recent Arabic summarization approaches that used ant colony and discriminant analysis algorithms. The proposed approach has obtained competitive results compared to those recent approaches.

Keywords: Multi-document text summarization; Arabic Language; Extractive-based summarization; Singular value decomposition (SVD); Fuzzy C-Means algorithm; Latent Dirichlet allocation (LDA) algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01783-2

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