Incorporating Prior Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization

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Proceedings titleProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Conference(SIGIR '09) The 32nd International ACM SIGIR Conference on research and development in Information Retrieval(SIGIR '09), Boston, MA, USA, July 19-23, 2009
Pages704705; # of pages: 2
SubjectInformation and Communications Technologies
AbstractThis paper presents a transductive approach to learn ranking functions for extractive multi-document summarization. At the first stage, the proposed approach identifies topic themes within a document collection, which help to identify two sets of relevant and irrelevant sentences to a question. It then iteratively trains a ranking function over these two sets of sentences by optimizing a ranking loss and fitting a prior model built on keywords. The output of the function is used to find further relevant and irrelevant sentences. This process is repeated until a desired stopping criterion is met.
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AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number16067309
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Record identifiere5d0c574-452d-417c-bea4-4fec9cbb8a7a
Record created2010-09-10
Record modified2016-05-09
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