Transductive learning over automatically detected themes for multi-document summarization

DOIResolve DOI: http://doi.org/10.1145/2009916.2010115
AuthorSearch for: ; Search for:
TypeArticle
Proceedings titleSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11, 24 July 2011 through 28 July 2011, Beijing
ISBN9781450309349
Pages11931194; # of pages: 2
SubjectData sets; Learning to rank; Multi-document summarization; Multiple documents; Mutli-document summarization; RankNet; Sentence extraction; Transductive learning; Information retrieval
AbstractWe 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.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology (IIT-ITI)
Peer reviewedYes
NPARC number21271352
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Record identifier5c0aa57a-fafb-422a-9604-8cd2eec28992
Record created2014-03-24
Record modified2016-05-09
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