| Download | - View accepted manuscript: Incorporating Prior Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization (PDF, 566 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1145/1571941.1572087 |
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| Author | Search for: Amini, Massih-Reza1; Search for: Usunier, Nicolas |
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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 | (SIGIR '09) The 32nd International ACM SIGIR Conference on research and development in Information Retrieval(SIGIR '09), Boston, MA, USA, July 19-23, 2009 |
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| Subject | Information and Communications Technologies |
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| Abstract | This 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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| Publication date | 2009 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 16067309 |
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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 | e5d0c574-452d-417c-bea4-4fec9cbb8a7a |
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| Record created | 2010-09-10 |
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| Record modified | 2020-04-16 |
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