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  • 标题:Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy
  • 本地全文:下载
  • 作者:Samer Abdulateef ; Naseer Ahmed Khan ; Bolin Chen
  • 期刊名称:Information
  • 电子版ISSN:2078-2489
  • 出版年度:2020
  • 卷号:11
  • 期号:2
  • 页码:59-71
  • DOI:10.3390/info11020059
  • 出版社:MDPI Publishing
  • 摘要:Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.
  • 关键词:arabic text summarization; multidocument text summarization; text clustering; word2vec arabic text summarization ; multidocument text summarization ; text clustering ; word2vec
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