| DOI | Resolve DOI: https://doi.org/10.1145/2009916.2010115 |
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| Author | Search for: Amini, M.-R.1; Search for: Usunier, N. |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | 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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| Subject | Data sets; Learning to rank; Multi-document summarization; Multiple documents; Mutli-document summarization; RankNet; Sentence extraction; Transductive learning; Information retrieval |
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| Abstract | 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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| Publication date | 2011 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21271352 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 5c0aa57a-fafb-422a-9604-8cd2eec28992 |
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| Record created | 2014-03-24 |
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| Record modified | 2020-04-21 |
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