Applying two-level reinforcement ranking in query-oriented multidocument summarization.
Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, th...
| Publicado en: | Journal of the American Society for Information Science & Technology Vol. 60; no. 10; pp. 2119 - 2132 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105445698&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105445698 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15322882 IGD jtl: Journal of the American Society for Information Science & Technology issn: 15322882 maglogo: Y pubinfo: dt: Oct2009 vid: 60 iid: 10 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105445698 2010428110 10.1002/asi.21127 105445698 ppf: 2119 ppct: 13 formats: tig: atl: Applying two-level reinforcement ranking in query-oriented multidocument summarization. aug: au: Wei F Li W Lu Q He Y affil: Department of Computing, Hong Kong Polytechnic University, Hong Kong, Department of Computer Science and Technology, Wuhan University, P.R. China; dsfwei@comp.polyu.edu.hk sug: subj: Abstracting and Indexing Methods Electronic Publications Internet Algorithms Utilization Funding Source Information Retrieval Human ab: Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, the newly emerging graph-based ranking algorithms (such as the PageRank-like algorithms) recursively compute sentence significance using the global information in a text graph that links sentences together. In general, the existing PageRank-like algorithms can model well the phenomena that a sentence is important if it is linked by many other important sentences. Or they are capable of modeling the mutual reinforcement among the sentences in the text graph. However, when dealing with multidocument summarization these algorithms often assemble a set of documents into one large file. The document dimension is totally ignored. In this article we present a framework to model the two-level mutual reinforcement among sentences as well as documents. Under this framework we design and develop a novel ranking algorithm such that the document reinforcement is taken into account in the process of sentence ranking. The convergence issue is examined. We also explore an interesting and important property of the proposed algorithm. When evaluated on the DUC 2005 and 2006 query-oriented multidocument summarization datasets, significant results are achieved. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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