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Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction

Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction

Nabil M. Hewahi, Kathrein Abu Kwaik
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 16
ISSN: 1947-9301|EISSN: 1947-931X|EISBN13: 9781466614543|DOI: 10.4018/jtd.2012040102
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MLA

Hewahi, Nabil M., and Kathrein Abu Kwaik. "Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction." IJTD vol.3, no.2 2012: pp.12-27. http://doi.org/10.4018/jtd.2012040102

APA

Hewahi, N. M. & Abu Kwaik, K. (2012). Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction. International Journal of Technology Diffusion (IJTD), 3(2), 12-27. http://doi.org/10.4018/jtd.2012040102

Chicago

Hewahi, Nabil M., and Kathrein Abu Kwaik. "Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction," International Journal of Technology Diffusion (IJTD) 3, no.2: 12-27. http://doi.org/10.4018/jtd.2012040102

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Abstract

Recently, the need has increased for an effective and powerful tool to automatically summarize text. For English and European languages an intensive works have been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However, Arabic language still suffers from the little attentions and research done in this filed. In this paper, we propose a model to automatically summarize Arabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of features, classify sentence based on scoring method, ranking sentences and finally generate an extracted summary. The main difference between the proposed system and other Arabic summarization systems are the consideration of semantics, entity objects such as names and places, and similarity factors in our proposed system. The proposed system has been applied on news domain using a dataset osbtained from Local newspaper. Manual evaluation techniques are used to evaluate and test the system. The results obtained by the proposed method achieve 86.5% similarity between the system and human summarization. A comparative study between our proposed system and Sakhr Arabic online summarization system has been conducted. The results show that our proposed system outperforms Shakr system.

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