research-article

Graph-based informative-sentence selection for opinion summarization

Authors:

Sinno Jialin Pan,

Dingxiong Deng,

Cyrus ShahabiAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 408 - 412

https://doi.org/10.1145/2492517.2492651

Published: 25 August 2013 Publication History

Abstract

In this paper, we propose a new framework for opinion summarization based on sentence selection. Our goal is to assist users to get helpful opinion suggestions from reviews by only reading a short summary with few informative sentences, where the quality of summary is evaluated in terms of both aspect coverage and viewpoints preservation. More specifically, we formulate the informative-sentence selection problem in opinion summarization as a community-leader detection problem, where a community consists of a cluster of sentences towards the same aspect of an entity. The detected leaders of the communities can be considered as the most informative sentences of the corresponding aspect, while informativeness of a sentence is defined by its informativeness within both its community and the document it belongs to. Review data from six product domains from Amazon.com are used to verify the effectiveness of our method for opinion summarization.

References

[1]

M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proceedings of the 10th ACM SIGKDD. New York, NY, USA: ACM, 2004, pp. 168--177.

Digital Library

[2]

Y. Lu, C. Zhai, and N. Sundaresan, "Rated aspect summarization of short comments," in Proceedings of the 18th WWW. New York, NY, USA: ACM, 2009, pp. 131--140.

Digital Library

[3]

Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai, "Topic sentiment mixture: modeling facets and opinions in weblogs," in Proceedings of the 16th WWW. New York, NY, USA: ACM, 2007, pp. 171--180.

Digital Library

[4]

A.-M. Popescu and O. Etzioni, "Extracting product features and opinions from reviews," in Proceedings of the HLT and EMNLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005, pp. 339--346.

Digital Library

[5]

B. Hu, Z. Song, and M. Ester, "User features and social networks for topic modeling in online social media," in ASONAM, 2012, pp. 202--209.

Digital Library

[6]

I. Titov and R. T. McDonald, "A joint model of text and aspect ratings for sentiment summarization," in ACL, 2008, pp. 308--316.

[7]

C. Danescu-Niculescu-Mizil, G. Kossinets, J. M. Kleinberg, and L. Lee, "How opinions are received by online communities: A case study on amazon.com helpfulness votes," in Proceedings of the 18th WWW. New York, NY, USA: ACM, 2009, pp. 141--150.

Digital Library

[8]

S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti, "Automatically assessing review helpfulness," in Proceedings of EMNLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006, pp. 423--430.

Digital Library

[9]

E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, "Detecting product review spammers using rating behaviors," in Proceedings of the 19th CIKM. New York, NY, USA: ACM, 2010, pp. 939--948.

Digital Library

[10]

J. Liu, Y. Cao, C. Y. Lin, Y. Huang, and M. Zhou, "Low-Quality product review detection in opinion summarization," in Proceedings of the Joint Conference on EMNLP-CoNLL, 2007, pp. 334--342.

[11]

S. Blair-goldensohn, T. Neylon, K. Hannan, G. A. Reis, R. Mcdonald, and J. Reynar, "Building a sentiment summarizer for local service reviews," in NLP in the Information Explosion Era, 2008.

[12]

K. Ganesan, C. Zhai, and J. Han, "Opinosis: A graph based approach to abstractive summarization of highly redundant opinions," in Proceedings of the 23rd COLING, 2010.

Digital Library

[13]

K. Lerman, S. Blair-Goldensohn, and R. McDonald, "Sentiment summarization: evaluating and learning user preferences," in Proceedings of the 12th EACL. Stroudsburg, PA, USA: ACL, 2009, pp. 514--522.

Digital Library

[14]

P. Tsaparas, A. Ntoulas, and E. Terzi, "Selecting a comprehensive set of reviews," in Proceedings of the 17th ACM SIGKDD. New York, NY, USA: ACM, 2011, pp. 168--176.

Digital Library

[15]

V. D. Blondel, J. loup Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," 2008.

[16]

P. J. McSweeney, K. Mehrotra, and J. C. Oh, "A game theoretic framework for community detection," in ASONAM, 2012, pp. 227--234.

Digital Library

[17]

M. Taboada, C. Anthony, and K. Voll, "Methods for creating semantic orientation dictionaries," in Proceedings of 5th ICLRE, Genoa, Italy, May 2006, pp. 427--432.

[18]

J. Cheng, Y. Ke, A. W.-C. Fu, J. X. Yu, and L. Zhu, "Finding maximal cliques in massive networks by h*-graph," in Proceedings of the SIGMOD. New York, NY, USA: ACM, 2010, pp. 447--458.

Digital Library

[19]

J. E. Hirsch, "An index to quantify an individual's scientific research output," Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 46, pp. 16569--16572, 2005.

[20]

Q. Lu and L. Getoor, "Link-based classification," in Proceedings of the 20th ICML. AAAI Press, Chicago, IL, USA, 2003, pp. 496--503.

[21]

L. C. Freeman, "Centrality in social networks: Conceptual clarification," Social Networks, vol. 1, no. 3, pp. 215--239, 1979.

[22]

P. Beineke, T. Hastie, C. Manning, and S. Vaithyanathan, "Exploring sentiment summarization," in AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, 2004.

[23]

R. Mihalcea and P. Tarau, "Textrank: Bringing order into text," in EMNLP, 2004, pp. 404--411.

Cited By

Garces DGil SDastani MSichman JAlechina NDignum V(2024)Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous RideshareProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662916(641-650)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662916
Lyu JDost KKoh YWicker J(2024)Regional bias in monolingual English language modelsMachine Learning10.1007/s10994-024-06555-6Online publication date: 9-Jul-2024
https://doi.org/10.1007/s10994-024-06555-6
Ahmad SLaroche M(2023)Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysisJournal of Marketing Analytics10.1057/s41270-023-00218-611:4(662-676)Online publication date: 8-Apr-2023
https://doi.org/10.1057/s41270-023-00218-6
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Index Terms

Graph-based informative-sentence selection for opinion summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 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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Published: 25 August 2013

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Defense Advanced Research Projects Agency

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ASONAM '13

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SIGKDD

ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Garces DGil SDastani MSichman JAlechina NDignum V(2024)Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous RideshareProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662916(641-650)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662916
Lyu JDost KKoh YWicker J(2024)Regional bias in monolingual English language modelsMachine Learning10.1007/s10994-024-06555-6Online publication date: 9-Jul-2024
https://doi.org/10.1007/s10994-024-06555-6
Ahmad SLaroche M(2023)Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysisJournal of Marketing Analytics10.1057/s41270-023-00218-611:4(662-676)Online publication date: 8-Apr-2023
https://doi.org/10.1057/s41270-023-00218-6
Mabrouk ARedondo RKayed M(2021)SEOpinion: Summarization and Exploration of Opinion from E-Commerce WebsitesSensors10.3390/s2102063621:2(636)Online publication date: 18-Jan-2021
https://doi.org/10.3390/s21020636
Rao AShah K(2020)An Unsupervised Technique to Generate Summaries from Opinionated Review DocumentsProgress in Advanced Computing and Intelligent Engineering10.1007/978-981-15-6353-9_35(388-397)Online publication date: 10-Nov-2020
https://doi.org/10.1007/978-981-15-6353-9_35
Dewdney NCotterill R(2018)Just the Facts: Winnowing Microblogs for Newsworthy Statements using Non-Lexical FeaturesComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-77116-8_29(391-403)Online publication date: 10-Oct-2018
https://doi.org/10.1007/978-3-319-77116-8_29
Al-Dhelaan MAl-Suhaim ASheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Sentiment diversification for short review summarizationProceedings of the International Conference on Web Intelligence10.1145/3106426.3106525(723-729)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106525
Jin JJi PYan S(2017)Comparison of series products from customer online concerns for competitive intelligenceJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-017-0635-9Online publication date: 27-Nov-2017
https://doi.org/10.1007/s12652-017-0635-9
Deerosejanadej CPhunchongharn PAchalakul T(2016)A sentence clustering framework for opinion summarization using a modified genetic algorithmProceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2016.7425925(269-272)Online publication date: 18-Jan-2016
https://dl.acm.org/doi/10.1109/BIGCOMP.2016.7425925
NASTASE VMIHALCEA RRADEV D(2015)A survey of graphs in natural language processingNatural Language Engineering10.1017/S135132491500034021:05(665-698)Online publication date: 12-Oct-2015
https://doi.org/10.1017/S1351324915000340
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