Document summarization via self-present sentence relevance model

Xiaodong LI; Shanfeng ZHU; Haoran XIE; Qing LI

doi:10.1007/978-3-642-37450-0_24

Document summarization via self-present sentence relevance model

Xiaodong LI
, Shanfeng ZHU^*
, Haoran XIE
, Qing LI

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

7 Citations (Scopus)

Abstract

Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences "present" themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 18th International Conference Proceedings
Editors	Weiyi MENG, Ling FENG, Stéphane BRESSAN, Werner WINIWARTER
Place of Publication	Berlin
Publisher	Springer Berlin Heidelberg
Pages	309-323
Number of pages	15
Volume	7826
ISBN (Electronic)	9783642374500
ISBN (Print)	9783642374494
DOIs	https://doi.org/10.1007/978-3-642-37450-0_24
Publication status	Published - 2013
Externally published	Yes
Event	The 18th International Conference on Database Systems for Advanced Applications - Wuhan, China Duration: 22 Apr 2013 → 25 Apr 2013 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=24802...CachedYou

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7826 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 18th International Conference on Database Systems for Advanced Applications
Abbreviated title	DASFAA 2013
Country/Territory	China
City	Wuhan
Period	22/04/13 → 25/04/13
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=24802...CachedYou

Keywords

Sentence relevance
Summarization

Access to Document

10.1007/978-3-642-37450-0_24

Cite this

LI, X., ZHU, S., XIE, H., & LI, Q. (2013). Document summarization via self-present sentence relevance model. In W. MENG, L. FENG, S. BRESSAN, & W. WINIWARTER (Eds.), Database Systems for Advanced Applications - 18th International Conference Proceedings (Vol. 7826, pp. 309-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7826 LNCS, No. PART 2). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_24

LI, Xiaodong ; ZHU, Shanfeng ; XIE, Haoran et al. / Document summarization via self-present sentence relevance model. Database Systems for Advanced Applications - 18th International Conference Proceedings. editor / Weiyi MENG ; Ling FENG ; Stéphane BRESSAN ; Werner WINIWARTER. Vol. 7826 Berlin : Springer Berlin Heidelberg, 2013. pp. 309-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Document summarization via self-present sentence relevance model",

abstract = "Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences {"}present{"} themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.",

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booktitle = "Database Systems for Advanced Applications - 18th International Conference Proceedings",

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LI, X, ZHU, S, XIE, H & LI, Q 2013, Document summarization via self-present sentence relevance model. in W MENG, L FENG, S BRESSAN & W WINIWARTER (eds), Database Systems for Advanced Applications - 18th International Conference Proceedings. vol. 7826, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7826 LNCS, Springer Berlin Heidelberg, Berlin, pp. 309-323, The 18th International Conference on Database Systems for Advanced Applications, Wuhan, China, 22/04/13. https://doi.org/10.1007/978-3-642-37450-0_24

Document summarization via self-present sentence relevance model. / LI, Xiaodong; ZHU, Shanfeng; XIE, Haoran et al.
Database Systems for Advanced Applications - 18th International Conference Proceedings. ed. / Weiyi MENG; Ling FENG; Stéphane BRESSAN; Werner WINIWARTER. Vol. 7826 Berlin: Springer Berlin Heidelberg, 2013. p. 309-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7826 LNCS, No. PART 2).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Document summarization via self-present sentence relevance model

AU - LI, Xiaodong

AU - ZHU, Shanfeng

AU - XIE, Haoran

AU - LI, Qing

PY - 2013

Y1 - 2013

N2 - Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences "present" themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.

AB - Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences "present" themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.

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KW - Summarization

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DO - 10.1007/978-3-642-37450-0_24

M3 - Conference paper (refereed)

AN - SCOPUS:84892899366

SN - 9783642374494

VL - 7826

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 309

EP - 323

BT - Database Systems for Advanced Applications - 18th International Conference Proceedings

A2 - MENG, Weiyi

A2 - FENG, Ling

A2 - BRESSAN, Stéphane

A2 - WINIWARTER, Werner

PB - Springer Berlin Heidelberg

CY - Berlin

T2 - The 18th International Conference on Database Systems for Advanced Applications

Y2 - 22 April 2013 through 25 April 2013

ER -

LI X, ZHU S, XIE H, LI Q. Document summarization via self-present sentence relevance model. In MENG W, FENG L, BRESSAN S, WINIWARTER W, editors, Database Systems for Advanced Applications - 18th International Conference Proceedings. Vol. 7826. Berlin: Springer Berlin Heidelberg. 2013. p. 309-323. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-37450-0_24

Document summarization via self-present sentence relevance model

Abstract

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Keywords

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