GDL

Journal from gdlhub / 2017-08-16 13:54:40
Oleh : Houda Oufaida, Omar Nouali , Philippe Blache, King Saud University
Dibuat : 2014-12-16, dengan 1 file

Keyword : Arabic text summarization Sentence extraction mRMR Minimum redundancy Maximum relevance
Url : http://www.sciencedirect.com/science/article/pii/S1319157814000329
Sumber pengambilan dokumen : web

Automatic text summarization aims to produce summaries for one or more texts using machine techniques. In this paper, we propose a novel statistical summarization system for Arabic texts. Our system uses a clustering algorithm and an adapted discriminant analysis method: mRMR (minimum redundancy and maximum relevance) to score terms. Through mRMR analysis, terms are ranked according to their discriminant and coverage power. Second, we propose a novel sentence extraction algorithm which selects sentences with top ranked terms and maximum diversity. Our system uses minimal language-dependant processing: sentence splitting, tokenization and root extraction. Experimental results on EASC and TAC 2011 MultiLingual datasets showed that our proposed approach is competitive to the state of the art systems.

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