Multilingual Text Summarization using Deep Learning
DOI:
https://doi.org/10.31695/IJERAT.2021.3712Keywords:
Summarization, TextRank, CNN, ROUGE, TAC-2011Abstract
Along with the extreme expansion of big data and the vast development of the internet, making documentation of the huge internet information is the first interest for people. These online textual data led to information overload and redundancy. Multi-document summarization is one of the solutions to such an issue, used to extract the main ideas of the documents and put them into a short summary. Summarizing documents should not affect the major concepts and the meaning of the original text. This paper proposes a new method for multi-document summarization. The basic idea of the proposed method relied on six different features to be extracted of each sentence in the studied collection, these features must be language. A set of the feature vectors is introduced to Convolutional Neural Networks (CNNs) for classification as either summary or non-summary sentences. A graph of summary sentences was generated and assigned scores by the TextRank algorithm. The implemented system was evaluated on both English and Arabic versions of the dataset of the TAC-2011 MultiLing Pilot by using ROUGE metrics. The proposed method achieved an average F-measure 0.46079, 0.20664 using ROUGE-1 and ROUGE-2 respectively, for English documents, and achieved an average F-measure 0.45624, 0.30725 for Arabic documents.
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Copyright (c) 2021 Rana Talib Al Timimi , Fatma Hassan Al Rubbiay
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