Minimum Redundancy and Maximum Relevance for Single and multi-document Arabic Text Summarization - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of King Saud University - Computer and Information Sciences Année : 2014

Minimum Redundancy and Maximum Relevance for Single and multi-document Arabic Text Summarization

Omar Nouali
  • Fonction : Auteur
  • PersonId : 1031945
Philippe Blache

Résumé

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.
Fichier principal
Vignette du fichier
OUFAIDA_JKSU_Elsevier_2014.pdf (1.54 Mo) Télécharger le fichier
Origine : Accord explicite pour ce dépôt
Loading...

Dates et versions

hal-01486088 , version 1 (31-10-2018)

Identifiants

Citer

Houda Oufaida, Omar Nouali, Philippe Blache. Minimum Redundancy and Maximum Relevance for Single and multi-document Arabic Text Summarization. Journal of King Saud University - Computer and Information Sciences, 2014, 26 (4), pp.450-461. ⟨10.1016/j.jksuci.2014.06.008⟩. ⟨hal-01486088⟩
212 Consultations
191 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More