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

被引:4
|
作者
Al-Taani, Ahmad T. T. [1 ]
Al-Sayadi, Sami H. H. [1 ]
机构
[1] Yarmouk Univ, Dept Comp Sci, Irbid, Jordan
关键词
Multi-document text summarization; Arabic Language; Extractive-based summarization; Singular value decomposition (SVD); Fuzzy C-Means algorithm; Latent Dirichlet allocation (LDA) algorithm; RANKING;
D O I
10.1007/s13198-022-01783-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:713 / 726
页数:14
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