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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Two-level classification for dialogue act recognition in task-oriented dialogues Blache, Philippe; Abderrahmane, Massina; Rauzy, Stéphane... In: COLING-2020 ; https://hal.archives-ouvertes.fr/hal-02954413 ; COLING-2020, Oct 2020, Barcelona, Spain (2020) BASE Show details 2 A coherence model for sentence ordering Oufaida, Houda; Blache, Philippe; Nouali, Omar In: Natural Language Processing and Information Systems 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, Salford, UK, June 26–28, 2019, Proceedings ; NLDB-2019 ; https://hal.archives-ouvertes.fr/hal-02299211 ; NLDB-2019, 2019, Manchester, United Kingdom. ⟨10.1007/978-3-030-23281-8⟩ (2019) BASE Show details 3 Résumé Automatique Multilingue. Expérimentations sur l'Anglais, l'Arabe et le Français Oufaida, Houda; Nouali, Omar; Blache, Philippe In: Traitement Automatique des Langues ; https://hal.archives-ouvertes.fr/hal-01507722 ; Traitement Automatique des Langues, 2014, Baltimore, United States (2014) BASE Show details 4 Minimum Redundancy and Maximum Relevance for Single and multi-document Arabic Text Summarization Oufaida, Houda; Nouali, Omar; Blache, Philippe In: ISSN: 1319-1578 ; EISSN: 1319-1578 ; Journal of King Saud University - Computer and Information Sciences ; https://hal.archives-ouvertes.fr/hal-01486088 ; Journal of King Saud University - Computer and Information Sciences, Elsevier 2014, 26 (4), pp.450-461. ⟨10.1016/j.jksuci.2014.06.008⟩ (2014) Abstract: International audience ; 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. Keyword: [SHS.INFO]Humanities and Social Sciences/Library and information sciences; Arabic text summarization; Maximum relevance; Minimum redundancy; mRMR; Sentence extraction; Text summarization URL: https://hal.archives-ouvertes.fr/hal-01486088/document https://hal.archives-ouvertes.fr/hal-01486088 https://doi.org/10.1016/j.jksuci.2014.06.008 https://hal.archives-ouvertes.fr/hal-01486088/file/OUFAIDA_JKSU_Elsevier_2014.pdf BASE Hide details

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