DOI | Trouver le DOI : https://doi.org/10.1145/2009916.2010115 |
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Auteur | Rechercher : Amini, M.-R.1; Rechercher : Usunier, N. |
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Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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Format | Texte, Article |
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Conférence | 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11, 24 July 2011 through 28 July 2011, Beijing |
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Sujet | Data sets; Learning to rank; Multi-document summarization; Multiple documents; Mutli-document summarization; RankNet; Sentence extraction; Transductive learning; Information retrieval |
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Résumé | We propose a new method for query-biased multi-document summarization, based on sentence extraction. The summary of multiple documents is created in two steps. Sentences are first clustered; where each cluster corresponds to one of the main themes present in the collection. Inside each theme, sentences are then ranked using a transductive learning-to-rank algorithm based on RankNet [2] in order to better identify those which are relevant to the query. The final summary contains the top-ranked sentences of each theme. Our approach is validated on DUC 2006 and DUC 2007 datasets. |
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Date de publication | 2011 |
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Dans | |
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Langue | anglais |
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Publications évaluées par des pairs | Oui |
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Numéro NPARC | 21271352 |
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Exporter la notice | Exporter en format RIS |
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Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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Identificateur de l’enregistrement | 5c0aa57a-fafb-422a-9604-8cd2eec28992 |
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Enregistrement créé | 2014-03-24 |
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Enregistrement modifié | 2020-04-21 |
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