More Web Proxy on the site http://driver.im/

research-article

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.

References

[1]

B. W. Bader and T. G. Kolda. Algorithm 862: MATLAB tensor classes for fast algorithm prototyping. ACM Trans. on Mathematical Software, 32(4):635--653, December 2006.

Digital Library

[2]

S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, and Z. Su. Optimizing Web search using social annotations. In Proc. of WWW'07, pages 501--510, Banff, Alberta, Canada, 2007.

Digital Library

[3]

R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In Proc. of the Intelligent Scalable Text Summarization Workshop, Madrid, Spain, 1997.

[4]

S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. In Proc. of WWW'98, pages 107--117, Brisbane, Australia, 1998.

Digital Library

[5]

G. Carenini, R. T. Ng, and X. Zhou. Summarizing email conversations with clue words. In Proc. of WWW'07, pages 91 -- 100, Banff, Alberta, Canada, 2007.

Digital Library

[6]

L. De Lathauwer, B. D. Moor, and J. Vandewall. A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 21(4):1253 -- 1278, 2000.

Digital Library

[7]

A. de Moor and L. Efimova. An argumentation analysis of weblog conversations. In Proc. of Int'l Working Conf. on the Language-Action Perspective on Communication Modelling (LAP'04), New Brunswick, NJ, 2004.

[8]

J.-Y. Delort. Identifying commented passages of documents using implicit hyperlinks. In Proc. of HYPERTEXT'06, pages 89 -- 98, Odense, Denmark, 2006.

Digital Library

[9]

G. Erken and D. R. Radev. LexPageRank: Prestige in multi-document text summarization. In Proc. of EMNLP'04, Barcelona, Spain, July 2004.

[10]

J. Goldstein, M. Kantrowitz, V. Mittal, and J. Carbonell. Summarizing text documents: Sentence selection and evaluation metrics. In Proc. of SIGIR'99, pages 121 -- 128, 1999.

Digital Library

[11]

E. Hovy and C.-Y. Lin. Automated text summarization and the SUMMARIST system. In Proc. of a workshop on held at Baltimore, Maryland, pages 197 -- 214, Baltimore, Maryland, 1996.

Digital Library

[12]

M. Hu, A. Sun, and E.-P. Lim. Comments-oriented blog summarization by sentence extraction. In Proc. of CIKM '07, pages 901 -- 904, Lisboa, Portugal, 2007.

Digital Library

[13]

K. Järvelin and J. Kekäläinen. IR evaluation methods for retrieving highly relevant documents. In Proc. of SIGIR '00, pages 41 -- 48, Athens, Greece, 2000.

Digital Library

[14]

T. G. Kolda and B. W. Bader. Tensor decompositions and applications. Technical Report SAND2007-6702, Sandia National Laboratories, Albuquerque, NM and Livermore, CA, November 2007.

[15]

G. G. Lee, J. Seo, S. Lee, H. Jung, B.-H. Cho, C. Lee, B.-K. Kwak, J. Cha, D. Kim, J. An, H. Kim, and K. Kim. SiteQ: Engineering high performance qa system using lexico-semantic pattern matching and shallow nlp. In Proc. of TREC'01, pages 437 -- 446, 2001.

[16]

C.-Y. Lin and E. Hovy. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proc. of NAACL'03, pages 71 -- 78, Edmonton, Canada, 2003.

Digital Library

[17]

H. P. Luhn. The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2):159 -- 165, 1958.

Digital Library

[18]

D. Marcu. The automatic construction of large-scale corpora for summarization research. In Proc. of SIGIR'99, pages 137 -- 144, Berkeley, California, USA, 1999.

Digital Library

[19]

R. Mihalcea and P. Tarau. TextRank: Bringing order into text. In Proc. of EMNLP'04, pages 404 -- 411, Barcelona, Spain, July 2004.

[20]

G. Mishne. Information access challenges in the blogspace. In Proc. of Int'l Workshop on Intelligent Information Access, Helsinki, Finland, 2006.

[21]

D. R. Radev, H. Jing, M. Stys;, and D. Tam. Centroid-based summarization of multiple documents. Information Processing & Management, 40(6):919 -- 938, 2004.

Digital Library

[22]

J.-T. Sun, D. Shen, H.-J. Zeng, Q. Yang, Y. Lu, and Z. Chen. Web-page summarization using clickthrough data. In Proc. of SIGIR'05, pages 194 -- 201, Salvador, Brazil, 2005.

Digital Library

[23]

X. Wan and J. Yang. CollabSum: exploiting multiple document clustering for collaborative single document summarizations. In Proc. of SIGIR'07, pages 143 -- 150, Amsterdam, The Netherlands, 2007.

Digital Library

[24]

L. Zhou and E. Hovy. On the summarization of dynamically introduced information: Online discussions and blogs. In Proc. of AAAI'06 Spring Symposium on Computational Approaches to Analyzing Weblogs, March 2006.

[25]

Q. Zhou, L. Sun, and J.-Y. Nie. IS SUM: A multi-document summarizer based on document index graphic and lexical chains. In DUC2005, 2005.

Cited By

Fendji JDonatien DAtemkeng M(2025)Hybrid Profile based Multi-document Text SummarisationProcedia Computer Science10.1016/j.procs.2025.01.047252(862-872)Online publication date: 2025
https://doi.org/10.1016/j.procs.2025.01.047
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
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

Recommendations

Topic-driven reader comments summarization
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking ...
Comments-oriented blog summarization by sentence extraction
CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

Much existing research on blogs focused on posts only, ignoring their comments. Our user study conducted on summarizing blog posts, however, showed that reading comments does change one's understanding about blog posts. In this research, we aim to ...
Recent advances in document summarization

The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '08

Sponsor:

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

July 20 - 24, 2008

Singapore, Singapore

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
1,096
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)3

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Fendji JDonatien DAtemkeng M(2025)Hybrid Profile based Multi-document Text SummarisationProcedia Computer Science10.1016/j.procs.2025.01.047252(862-872)Online publication date: 2025
https://doi.org/10.1016/j.procs.2025.01.047
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten