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Title: An efficient single document Arabic text summarization using a combination of statistical and semantic features
Authors: Quaroush, Aziz 
Abu Farha, Ibrahim 
Ghanem, Wasel 
Washaha, Mahdi 
Maali, Eman 
Keywords: Arabic language - Computer-assisted instruction;Machine learning - Technique;Cryptography;Semantics;Arabic language - Data processing;Arabic language - Programmed instruction
Issue Date: 26-Mar-2019
Publisher: Science Direct
Journal: Journal of King Saud University 
Abstract: The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel for-mulation is used taking into account sentence importance, coverage and diversity. Further, two summa-rizing techniques including score-based and supervised machine learning were employed to produce thesummary and then assist leveraging the designed features. We demonstrate the effectiveness of the pro-posed method through a set of experiments under EASC corpus using ROUGE measure. Compared tosome existing related work, the experimental evaluation shows the strength of the proposed methodin terms of precision, recall, and F-score performance metrics.
Description: Article to be published in : Journal of King Saud University – Computer and Information Sciences
Appears in Collections:Fulltext Publications

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