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

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.