Minimum redundancy and maximum relevance for single and multi-document Arabic text summarization

• Published in: Journal of King Saud University: Computer and Information Sciences
• Main Authors: Houda Oufaida, Omar Nouali, Philippe Blache
• Format: Article
• Language: English
• Published: Springer 2014-12-01
• Subjects: Arabic text summarization; Sentence extraction; mRMR; Minimum redundancy; Maximum relevance
• Online Access: http://www.sciencedirect.com/science/article/pii/S1319157814000329

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.