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Comments-oriented document summarization: understanding documents with readers' feedback

Authors:

Ee-Peng LimAuthors Info & Claims

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 291 - 298

https://doi.org/10.1145/1390334.1390385

Published: 20 July 2008 Publication History

Abstract

Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.

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Cited By

Kumar RChakraborty RTiwari ASaha SSaini NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment SensitivityProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614849(1117-1126)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614849
Wang GWu W(2023)Surveying the landscape of text summarization with deep learning: A comprehensive reviewDiscrete Mathematics, Algorithms and Applications10.1142/S179383092330004716:03Online publication date: 20-Dec-2023
https://doi.org/10.1142/S1793830923300047
Roha VSaini NSaha SMoreno J(2023)MOO-CMDS+NER: Named Entity Recognition-Based Extractive Comment-Oriented Multi-document SummarizationAdvances in Information Retrieval10.1007/978-3-031-28238-6_49(580-588)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-28238-6_49
Show More Cited By

Index Terms

Comments-oriented document summarization: understanding documents with readers' feedback
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Information

Published In

cover image ACM Conferences

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 July 2008

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SIGIR '08

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Kumar RChakraborty RTiwari ASaha SSaini NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment SensitivityProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614849(1117-1126)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614849
Wang GWu W(2023)Surveying the landscape of text summarization with deep learning: A comprehensive reviewDiscrete Mathematics, Algorithms and Applications10.1142/S179383092330004716:03Online publication date: 20-Dec-2023
https://doi.org/10.1142/S1793830923300047
Roha VSaini NSaha SMoreno J(2023)MOO-CMDS+NER: Named Entity Recognition-Based Extractive Comment-Oriented Multi-document SummarizationAdvances in Information Retrieval10.1007/978-3-031-28238-6_49(580-588)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-28238-6_49
Nguyen MTran DPhan VNguyen L(2023)Towards Social Context Summarization with Convolutional Neural NetworksComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_27(341-353)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_27
Roha VSaini NSaha SMoreno JWagner M(2022)Unsupervised framework for comment-based multi-document extractive summarizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528691(574-582)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528691
Huang YYu ZXiang YYu ZGuo J(2022)Exploiting comments information to improve legal public opinion news abstractive summarizationFrontiers of Computer Science10.1007/s11704-021-0561-z16:6Online publication date: 22-Jan-2022
https://doi.org/10.1007/s11704-021-0561-z
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
Zheng XSun AMuthuswamy K(2021)Tweet-aware News Summarization with Dual-Attention MechanismCompanion Proceedings of the Web Conference 202110.1145/3442442.3452309(473-480)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452309
Belwal RRai SGupta A(2021)Text summarization using topic-based vector space model and semantic measureInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10253658:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102536
Nguyen MNguyen VVu HNguyen V(2020)Transformer-based Summarization by Exploiting Social Information2020 12th International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE50997.2020.9287388(25-30)Online publication date: 12-Nov-2020
https://doi.org/10.1109/KSE50997.2020.9287388
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