research-article

Generating comparative summaries of contradictory opinions in text

Authors:

ChengXiang ZhaiAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 385 - 394

https://doi.org/10.1145/1645953.1646004

Published: 02 November 2009 Publication History

Abstract

This paper presents a study of a novel summarization problem called contrastive opinion summarization (COS). Given two sets of positively and negatively opinionated sentences which are often the output of an existing opinion summarizer, COS aims to extract comparable sentences from each set of opinions and generate a comparative summary containing a set of contrastive sentence pairs. We formally formulate the problem as an optimization problem and propose two general methods for generating a comparative summary using the framework, both of which rely on measuring the content similarity and contrastive similarity of two sentences. We study several strategies to compute these two similarities. We also create a test data set for evaluating such a novel summarization problem. Experiment results on this test set show that the proposed methods are effective for generating comparative summaries of contradictory opinions.

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Cited By

Wankhade MKulkarni CRao A(2024)A survey on aspect base sentiment analysis methods and challengesApplied Soft Computing10.1016/j.asoc.2024.112249167(112249)Online publication date: Dec-2024
https://doi.org/10.1016/j.asoc.2024.112249
Ströhle TCampos RJatowt A(2023)Contrastive text summarization: a surveyInternational Journal of Data Science and Analytics10.1007/s41060-023-00434-4Online publication date: 9-Aug-2023
https://doi.org/10.1007/s41060-023-00434-4
Abu-Salih BAlweshah MAlazab MAl-Okaily MAlahmari MAl-Habashneh MAl-Sharaeh S(2023)Natural language inference model for customer advocacy detection in online customer engagementMachine Learning10.1007/s10994-023-06476-w113:4(2249-2275)Online publication date: 29-Nov-2023
https://doi.org/10.1007/s10994-023-06476-w
Show More Cited By

Index Terms

Generating comparative summaries of contradictory opinions in text
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 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: 02 November 2009

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CIKM '09: Conference on Information and Knowledge Management

November 2 - 6, 2009

Hong Kong, China

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Cited By

Wankhade MKulkarni CRao A(2024)A survey on aspect base sentiment analysis methods and challengesApplied Soft Computing10.1016/j.asoc.2024.112249167(112249)Online publication date: Dec-2024
https://doi.org/10.1016/j.asoc.2024.112249
Ströhle TCampos RJatowt A(2023)Contrastive text summarization: a surveyInternational Journal of Data Science and Analytics10.1007/s41060-023-00434-4Online publication date: 9-Aug-2023
https://doi.org/10.1007/s41060-023-00434-4
Abu-Salih BAlweshah MAlazab MAl-Okaily MAlahmari MAl-Habashneh MAl-Sharaeh S(2023)Natural language inference model for customer advocacy detection in online customer engagementMachine Learning10.1007/s10994-023-06476-w113:4(2249-2275)Online publication date: 29-Nov-2023
https://doi.org/10.1007/s10994-023-06476-w
Pham D(2022)Exploring the Effect of Word Embeddings and Bag-of-Words for Vietnamese Sentiment AnalysisUbiquitous Intelligent Systems10.1007/978-981-19-2541-2_49(595-605)Online publication date: 26-Jul-2022
https://doi.org/10.1007/978-981-19-2541-2_49
Uddin GBaysal OGuerrouj LKhomh F(2021)Understanding How and Why Developers Seek and Analyze API-Related OpinionsIEEE Transactions on Software Engineering10.1109/TSE.2019.290303947:4(694-735)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TSE.2019.2903039
Lee Y(2021)Extraction of Competitive Factors in a Competitor Analysis Using an Explainable Neural NetworkNeural Processing Letters10.1007/s11063-021-10499-6Online publication date: 23-Mar-2021
https://doi.org/10.1007/s11063-021-10499-6
Kim YJang MAllan J(2020)Explaining Text Matching on Neural Natural Language InferenceACM Transactions on Information Systems10.1145/341805238:4(1-23)Online publication date: 16-Sep-2020
https://dl.acm.org/doi/10.1145/3418052
Liu B(2020)Sentiment Analysis10.1017/9781108639286Online publication date: 23-Sep-2020
https://doi.org/10.1017/9781108639286
Missen MCoustaty MAsmat HFirdous AAkhtar NAkram MPrasath V(2020)2Es of TISRecommender System with Machine Learning and Artificial Intelligence10.1002/9781119711582.ch3(45-70)Online publication date: 15-Jul-2020
https://doi.org/10.1002/9781119711582.ch3
Jin JLiu YJi PKwong C(2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1Online publication date: 17-Sep-2018
https://doi.org/10.1115/1.4041087
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