research-article

Latent dirichlet allocation based multi-document summarization

Authors:

Balaraman RavindranAuthors Info & Claims

AND '08: Proceedings of the second workshop on Analytics for noisy unstructured text data

Pages 91 - 97

https://doi.org/10.1145/1390749.1390764

Published: 24 July 2008 Publication History

Abstract

Extraction based Multi-Document Summarization Algorithms consist of choosing sentences from the documents using some weighting mechanism and combining them into a summary. In this article we use Latent Dirichlet Allocation to capture the events being covered by the documents and form the summary with sentences representing these different events. Our approach is distinguished from existing approaches in that we use mixture models to capture the topics and pick up the sentences without paying attention to the details of grammar and structure of the documents. Finally we present the evaluation of the algorithms on the DUC 2002 Corpus multi-document summarization tasks using the ROUGE evaluator to evaluate the summaries. Compared to DUC 2002 winners, our algorithms gave significantly better ROUGE-1 recall measures.

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Cited By

Yang YTan YMin JHuang Z(2023)Automatic text summarization for government news reports based on multiple featuresThe Journal of Supercomputing10.1007/s11227-023-05599-080:3(3212-3228)Online publication date: 30-Aug-2023
https://doi.org/10.1007/s11227-023-05599-0
Sim YKim JChoi JHuh J(2022)System Design for Detecting Real Estate Speculation Abusing Inside Information: For the Fair Reallocation of LandLand10.3390/land1104056511:4(565)Online publication date: 11-Apr-2022
https://doi.org/10.3390/land11040565
Kondath MSuseelan DIdicula S(2022)Extractive summarization of Malayalam documents using latent Dirichlet allocation: An experienceJournal of Intelligent Systems10.1515/jisys-2022-002731:1(393-406)Online publication date: 29-Mar-2022
https://doi.org/10.1515/jisys-2022-0027
Show More Cited By

Index Terms

Latent dirichlet allocation based multi-document summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Published In

cover image ACM Other conferences

AND '08: Proceedings of the second workshop on Analytics for noisy unstructured text data

July 2008

130 pages

ISBN:9781605581965

DOI:10.1145/1390749

Conference Chairs:
Daniel Lopresti
Lehigh University
,
Shourya Roy
IBM India Research Lab
,
Klaus Schulz
University of Munich
,
L. Venkata Subramaniam
India Research Lab

Copyright © 2008 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2008

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AND '08

AND '08: Second Workshop on Analytics for Noisy Unstructured Text Data

July 24, 2008

Singapore

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Overall Acceptance Rate 15 of 22 submissions, 68%

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Cited By

Yang YTan YMin JHuang Z(2023)Automatic text summarization for government news reports based on multiple featuresThe Journal of Supercomputing10.1007/s11227-023-05599-080:3(3212-3228)Online publication date: 30-Aug-2023
https://doi.org/10.1007/s11227-023-05599-0
Sim YKim JChoi JHuh J(2022)System Design for Detecting Real Estate Speculation Abusing Inside Information: For the Fair Reallocation of LandLand10.3390/land1104056511:4(565)Online publication date: 11-Apr-2022
https://doi.org/10.3390/land11040565
Kondath MSuseelan DIdicula S(2022)Extractive summarization of Malayalam documents using latent Dirichlet allocation: An experienceJournal of Intelligent Systems10.1515/jisys-2022-002731:1(393-406)Online publication date: 29-Mar-2022
https://doi.org/10.1515/jisys-2022-0027
Akhtar NSufyan Beg MJaved HHussain M(2022)Tiered sentence based topic model for multi-document summarizationJournal of Information and Optimization Sciences10.1080/02522667.2022.213321943:8(2131-2141)Online publication date: 16-Dec-2022
https://doi.org/10.1080/02522667.2022.2133219
Belwal RRai SGupta A(2022)Extractive text summarization using clustering-based topic modelingSoft Computing10.1007/s00500-022-07534-627:7(3965-3982)Online publication date: 4-Oct-2022
https://doi.org/10.1007/s00500-022-07534-6
Li DYin HWang CSong SLi KLi C(2021)OpinionManager: Visual Exploration of Online Reviews in P2P AccommodationProceedings of the 14th International Symposium on Visual Information Communication and Interaction10.1145/3481549.3481551(1-8)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3481549.3481551
He DRen YMateen Khattak ALiu XTao SGao W(2021)Automatic topic labeling using graph-based pre-trained neural embeddingNeurocomputing10.1016/j.neucom.2021.08.078463:C(596-608)Online publication date: 6-Nov-2021
https://dl.acm.org/doi/10.1016/j.neucom.2021.08.078
Zhao HLi YLiu FXie XChen L(2021)State and tendency: an empirical study of deep learning question&answer topics on Stack OverflowScience China Information Sciences10.1007/s11432-019-3018-664:11Online publication date: 15-Oct-2021
https://doi.org/10.1007/s11432-019-3018-6
Belwal RRai SGupta A(2020)A new graph-based extractive text summarization using keywords or topic modelingJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02591-xOnline publication date: 17-Oct-2020
https://doi.org/10.1007/s12652-020-02591-x
Manh HLe Thanh HMinh T(2019)Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence PositionProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369688(29-35)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3368926.3369688
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