LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization

Dehong Gao; Wenjie Li; You Ouyang; Renxian Zhang

doi:10.1007/978-3-642-35341-3_33

LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization

Dehong Gao, Wenjie Li, You Ouyang, Renxian Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

8 Citations (Scopus)

Abstract

In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.

Original language	English
Title of host publication	Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
Pages	376-385
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-35341-3_33
Publication status	Published - 31 Dec 2012
Event	8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China Duration: 17 Dec 2012 → 19 Dec 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7675 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Asia Information Retrieval Societies Conference, AIRS 2012
Country/Territory	China
City	Tianjin
Period	17/12/12 → 19/12/12

Keywords

Graph-based sentence ranking
Latent Dirichlet Allocation
Multi-document summarization

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

More information

10.1007/978-3-642-35341-3_33

Cite this

Gao, D., Li, W., Ouyang, Y., & Zhang, R. (2012). LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization. In Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings (pp. 376-385). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7675 LNCS). https://doi.org/10.1007/978-3-642-35341-3_33

Gao, Dehong ; Li, Wenjie ; Ouyang, You et al. / LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization. Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings. 2012. pp. 376-385 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.",

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author = "Dehong Gao and Wenjie Li and You Ouyang and Renxian Zhang",

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note = "8th Asia Information Retrieval Societies Conference, AIRS 2012 ; Conference date: 17-12-2012 Through 19-12-2012",

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Gao, D, Li, W, Ouyang, Y & Zhang, R 2012, LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization. in Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7675 LNCS, pp. 376-385, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, 17/12/12. https://doi.org/10.1007/978-3-642-35341-3_33

LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization. / Gao, Dehong; Li, Wenjie; Ouyang, You et al.
Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings. 2012. p. 376-385 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7675 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Gao, Dehong

AU - Li, Wenjie

AU - Ouyang, You

AU - Zhang, Renxian

PY - 2012/12/31

Y1 - 2012/12/31

N2 - In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.

AB - In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.

KW - Graph-based sentence ranking

KW - Latent Dirichlet Allocation

KW - Multi-document summarization

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DO - 10.1007/978-3-642-35341-3_33

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

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Gao D, Li W, Ouyang Y, Zhang R. LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization. In Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings. 2012. p. 376-385. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-35341-3_33

LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization

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