Applying two-level reinforcement ranking in query-oriented multidocument summarization.
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| Title: | Applying two-level reinforcement ranking in query-oriented multidocument summarization. |
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| Authors: | Furu Wei1,2 csfwei@comp.polyu.edu.hk, Wenjie Li1 cswjli@comp.polyu.edu.hk, Qin Lu1 csluqin@comp.polyu.edu.hk, Yanxiang He2 yxhe@whu.edu.cn |
| Source: | Journal of the American Society for Information Science & Technology. Oct2009, Vol. 60 Issue 10, p2119-2131. 13p. 4 Diagrams, 13 Charts. |
| Subjects: | Software documentation, Algorithms, Computer software, Information science, Computer science, Computer systems, Computer engineering |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 44295775 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Applying two-level reinforcement ranking in query-oriented multidocument summarization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Furu+Wei%22">Furu Wei</searchLink><relatesTo>1,2</relatesTo><i> csfwei@comp.polyu.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Wenjie+Li%22">Wenjie Li</searchLink><relatesTo>1</relatesTo><i> cswjli@comp.polyu.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Qin+Lu%22">Qin Lu</searchLink><relatesTo>1</relatesTo><i> csluqin@comp.polyu.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Yanxiang+He%22">Yanxiang He</searchLink><relatesTo>2</relatesTo><i> yxhe@whu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Oct2009, Vol. 60 Issue 10, p2119-2131. 13p. 4 Diagrams, 13 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+documentation%22">Software documentation</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Information+science%22">Information science</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%22">Computer engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asi.21127 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2119 Subjects: – SubjectFull: Software documentation Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Computer software Type: general – SubjectFull: Information science Type: general – SubjectFull: Computer science Type: general – SubjectFull: Computer systems Type: general – SubjectFull: Computer engineering Type: general Titles: – TitleFull: Applying two-level reinforcement ranking in query-oriented multidocument summarization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Furu Wei – PersonEntity: Name: NameFull: Wenjie Li – PersonEntity: Name: NameFull: Qin Lu – PersonEntity: Name: NameFull: Yanxiang He IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 15322882 Numbering: – Type: volume Value: 60 – Type: issue Value: 10 Titles: – TitleFull: Journal of the American Society for Information Science & Technology Type: main |
| ResultId | 1 |
