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Ranking explanatory sentences for opinion summarization

Authors:

Riddhiman GhoshAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 1069 - 1072

https://doi.org/10.1145/2484028.2484172

Published: 28 July 2013 Publication History

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Abstract

We introduce a novel sentence ranking problem called explanatory sentence extraction (ESE) which aims to rank sentences in opinionated text based on their usefulness for helping users understand the detailed reasons of sentiments (i.e., "explanatoriness"). We propose and study several general methods for scoring the explanatoriness of a sentence. We create new data sets and propose a new measure for evaluation. Experiment results show that the proposed methods are effective, outperforming a state of the art sentence ranking method for standard text summarization.

References

[1]

X. Ding, B. Liu, and P. S. Yu. A holistic lexicon-based approach to opinion mining. In WSDM '08: Proceedings of the 1st international conference on Web search and web data mining, pages 231--240, New York, NY, USA, 2008. ACM.

Digital Library

Google Scholar

[2]

G. Erkan and D. R. Radev. Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res., 22(1):457--479, 2004.

Digital Library

Google Scholar

[3]

E. Hovy and C.-Y. Lin. Automated text summarization in SUMMARIST. In I. Mani and M. T. Maybury, editors, Advances in Automatic Text Summarization. MIT Press, 1999.

Google Scholar

[4]

M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04: Proceedings of the 10th international conference on Knowledge discovery and data mining, pages 168--177, New York, NY, USA, 2004. ACM.

Digital Library

Google Scholar

[5]

M. Hu and B. Liu. Mining opinion features in customer reviews. In AAAI'04: Proceedings of the 19th national conference on Artifical intelligence, pages 755--760. AAAI Press, 2004.

Digital Library

Google Scholar

[6]

M. Hu and B. Liu. Opinion extraction and summarization on the web. In AAAI'06: proceedings of the 21st national conference on Artificial intelligence, pages 1621--1624. AAAI Press, 2006.

Digital Library

Google Scholar

[7]

K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: development and comparative experiments. Inf. Process. Manage., 36(6):779--808, Nov. 2000.

Digital Library

Google Scholar

[8]

J. Kupiec, J. Pedersen, and F. Chen. A trainable document summarizer. In SIGIR '95: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pages 68--73, New York, NY, USA, 1995. ACM.

Digital Library

Google Scholar

[9]

C. D. Paice. Constructing literature abstracts by computer: techniques and prospects. Inf. Process. Manage., 26(1):171--186, 1990.

Digital Library

Google Scholar

Cited By

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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
Spitz AFeher GGertz M(2017)Extracting Descriptions of Location Relations from Implicit Textual NetworksProceedings of the 11th Workshop on Geographic Information Retrieval10.1145/3155902.3155909(1-9)Online publication date: 30-Nov-2017
https://dl.acm.org/doi/10.1145/3155902.3155909
Tan JKotov APir Mohammadiani RHuo YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Sentence Retrieval with Sentiment-specific Topical Anchoring for Review SummarizationProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133153(2323-2326)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133153
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Index Terms

Ranking explanatory sentences for opinion summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

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

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Published: 28 July 2013

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

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

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Cited By

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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
Spitz AFeher GGertz M(2017)Extracting Descriptions of Location Relations from Implicit Textual NetworksProceedings of the 11th Workshop on Geographic Information Retrieval10.1145/3155902.3155909(1-9)Online publication date: 30-Nov-2017
https://dl.acm.org/doi/10.1145/3155902.3155909
Tan JKotov APir Mohammadiani RHuo YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Sentence Retrieval with Sentiment-specific Topical Anchoring for Review SummarizationProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133153(2323-2326)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133153
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
Ren ZInel OAroyo Lde Rijke MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Time-aware Multi-Viewpoint Summarization of Multilingual Social Text StreamsProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983710(387-396)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983710
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
Vo TLe BLe Nguyen M(2016)Scoring Explanatoriness of a Sentence and Ranking for Explanatory Opinion SummaryRecent Developments in Intelligent Information and Database Systems10.1007/978-3-319-31277-4_24(277-286)Online publication date: 27-Feb-2016
https://doi.org/10.1007/978-3-319-31277-4_24
Braunstain LKurland OCarmel DSzpektor IShtok A(2016)Supporting Human Answers for Advice-Seeking Questions in CQA SitesAdvances in Information Retrieval10.1007/978-3-319-30671-1_10(129-141)Online publication date: 2016
https://doi.org/10.1007/978-3-319-30671-1_10
Ren Zde Rijke MBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Summarizing Contrastive Themes via Hierarchical Non-Parametric ProcessesProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767713(93-102)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767713
He YSong JNan YFu G(2015)Clustering Chinese Product Features with Multilevel SimilarityChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data10.1007/978-3-319-25816-4_28(347-355)Online publication date: 13-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-25816-4_28
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Compact explanatory opinion summarization

Opinion summarization methods

Opinion summarization on spontaneous conversations

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