Sentences optimum selection for multi-document summarization

Bing Qin; Ting Liu; Shanglin Chen; Sheng Li

doi:10.1360/crad20060625

Sentences optimum selection for multi-document summarization

Bing Qin^*
, Ting Liu
, Shanglin Chen
, Sheng Li

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

An approach for sentence optimum selection based on sub-topics of multi-documents is proposed. Multi-documents can be clustered into sub-topics after sentence similarity calculation, which can be sorted by scoring. Then sentences from all sub-topics are selected in order to get maximum coverage ratio of effective words. Using this method, the information redundancy of each sub-topic and among sub-topics is reduced. The information coverage ratio of the summarization is better improved. The experiment shows that the result is satisfied.

Original language	English
Pages (from-to)	1129-1134
Number of pages	6
Journal	Jisuanji Yanjiu yu Fazhan/Computer Research and Development
Volume	43
Issue number	6
DOIs	https://doi.org/10.1360/crad20060625
State	Published - Jun 2006
Externally published	Yes

Keywords

Multi-document summarization
Sentence optimum selection
Sub-topic

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10.1360/crad20060625

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title = "Sentences optimum selection for multi-document summarization",

abstract = "An approach for sentence optimum selection based on sub-topics of multi-documents is proposed. Multi-documents can be clustered into sub-topics after sentence similarity calculation, which can be sorted by scoring. Then sentences from all sub-topics are selected in order to get maximum coverage ratio of effective words. Using this method, the information redundancy of each sub-topic and among sub-topics is reduced. The information coverage ratio of the summarization is better improved. The experiment shows that the result is satisfied.",

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AU - Qin, Bing

AU - Liu, Ting

AU - Chen, Shanglin

AU - Li, Sheng

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N2 - An approach for sentence optimum selection based on sub-topics of multi-documents is proposed. Multi-documents can be clustered into sub-topics after sentence similarity calculation, which can be sorted by scoring. Then sentences from all sub-topics are selected in order to get maximum coverage ratio of effective words. Using this method, the information redundancy of each sub-topic and among sub-topics is reduced. The information coverage ratio of the summarization is better improved. The experiment shows that the result is satisfied.

AB - An approach for sentence optimum selection based on sub-topics of multi-documents is proposed. Multi-documents can be clustered into sub-topics after sentence similarity calculation, which can be sorted by scoring. Then sentences from all sub-topics are selected in order to get maximum coverage ratio of effective words. Using this method, the information redundancy of each sub-topic and among sub-topics is reduced. The information coverage ratio of the summarization is better improved. The experiment shows that the result is satisfied.

KW - Multi-document summarization

KW - Sentence optimum selection

KW - Sub-topic

UR - https://www.scopus.com/pages/publications/33747077457

U2 - 10.1360/crad20060625

DO - 10.1360/crad20060625

M3 - 文章

AN - SCOPUS:33747077457

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JO - Jisuanji Yanjiu yu Fazhan/Computer Research and Development

JF - Jisuanji Yanjiu yu Fazhan/Computer Research and Development

IS - 6

ER -

Sentences optimum selection for multi-document summarization

Abstract

Keywords

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