A document-sensitive graph model for multi-document summarization
In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influen...
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| Published in: | Knowledge and information systems Vol. 22; no. 2; pp. 245 - 259 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Springer-Verlag
01.02.2010
Springer Springer Nature B.V |
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| ISSN: | 0219-1377, 0219-3116 |
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| Abstract | In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document–document and document–sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks. |
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| AbstractList | In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document-document and document-sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks. [PUBLICATION ABSTRACT] In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document–document and document–sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks. |
| Author | He, Yanxiang Wei, Furu Li, Wenjie Lu, Qin |
| Author_xml | – sequence: 1 givenname: Furu surname: Wei fullname: Wei, Furu organization: Department of Computing, The Hong Kong Polytechnic University, Department of Computer Science and Technology, Wuhan University – sequence: 2 givenname: Wenjie surname: Li fullname: Li, Wenjie email: cswjli@comp.polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University – sequence: 3 givenname: Qin surname: Lu fullname: Lu, Qin organization: Department of Computing, The Hong Kong Polytechnic University – sequence: 4 givenname: Yanxiang surname: He fullname: He, Yanxiang organization: Department of Computer Science and Technology, Wuhan University |
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| Cites_doi | 10.1016/S0169-7552(98)00110-X 10.1145/1046456.1046461 10.1137/S0036144599359449 10.1109/TKDE.2003.1208999 10.1145/324133.324140 10.1007/s10115-007-0094-2 10.1007/s10115-007-0114-2 10.1016/j.ipm.2003.10.006 10.1145/1183614.1183703 10.3115/1073445.1073465 10.1080/15427951.2004.10129091 10.3115/1220575.1220690 10.1109/WI.2005.166 10.1007/11940098_13 10.1613/jair.1523 10.1109/WI.2006.182 |
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| Keywords | Query-oriented summarization Graph-based ranking algorithm Inter- and intra-document relation Graph-based summarization model Generic summarization Database query Hierarchical classification Abstract Transmission protocol Iterative method Graph theory Modeling Document structure Linguistics Sentence Document analysis |
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