Transductive learning over automatically detected themes for multi-document summarization

Authors:
Massih-Reza Amini;Nicolas Usunier
Affiliations:
National Research Council of Canada, Gatineau, PQ, Canada;Laboratorie d'Informatique de Paris 6, Paris, France
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

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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, 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.