research-article

Compact explanatory opinion summarization

Authors:

Malu Castellanos,

ChengXiang Zhai,

Umeshwar Dayal,

Riddhiman GhoshAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1697 - 1702

https://doi.org/10.1145/2505515.2505596

Published: 27 October 2013 Publication History

Abstract

In this paper, we propose a novel opinion summarization problem called compact explanatory opinion summarization (CEOS) which aims to extract within-sentence explanatory text segments from input opinionated texts to help users better understand the detailed reasons of sentiments. We propose and study general methods for identifying candidate boundaries and scoring the explanatoriness of text segments using Hidden Markov Models. We create new data sets and use a new evaluation measure to evaluate CEOS. Experimental results show that the proposed methods are effective for generating an explanatory opinion summary, outperforming a standard text summarization method.

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Cited By

Agathangelou PKatakis IKoutoulakis IKokkoras FGunopulos D(2018)Learning patterns for discovering domain-oriented opinion wordsKnowledge and Information Systems10.1007/s10115-017-1072-y55:1(45-77)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1072-y
Pan DWang MZhang MFu G(2017)Extracting Chinese Explanatory Expressions with Discrete and Neural CRFsData Science10.1007/978-981-10-6388-6_1(1-12)Online publication date: 16-Sep-2017
https://doi.org/10.1007/978-981-10-6388-6_1
Zhang LHe YFu G(2016)Extracting explanatory segments from Chinese product reviews via semantic pattern matching2016 International Conference on Asian Language Processing (IALP)10.1109/IALP.2016.7875950(124-128)Online publication date: Nov-2016
https://doi.org/10.1109/IALP.2016.7875950

Index Terms

Compact explanatory opinion summarization
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

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Agathangelou PKatakis IKoutoulakis IKokkoras FGunopulos D(2018)Learning patterns for discovering domain-oriented opinion wordsKnowledge and Information Systems10.1007/s10115-017-1072-y55:1(45-77)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1072-y
Pan DWang MZhang MFu G(2017)Extracting Chinese Explanatory Expressions with Discrete and Neural CRFsData Science10.1007/978-981-10-6388-6_1(1-12)Online publication date: 16-Sep-2017
https://doi.org/10.1007/978-981-10-6388-6_1
Zhang LHe YFu G(2016)Extracting explanatory segments from Chinese product reviews via semantic pattern matching2016 International Conference on Asian Language Processing (IALP)10.1109/IALP.2016.7875950(124-128)Online publication date: Nov-2016
https://doi.org/10.1109/IALP.2016.7875950

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