research-article

Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes

Authors:

Maarten de RijkeAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 93 - 102

https://doi.org/10.1145/2766462.2767713

Published: 09 August 2015 Publication History

Abstract

Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints. We address the task of summarizing contrastive themes: given a set of opinionated documents, select meaningful sentences to represent contrastive themes present in those documents. Several factors make this a challenging problem: unknown numbers of topics, unknown relationships among topics, and the extraction of comparative sentences. Our approach has three core ingredients: contrastive theme modeling, diverse theme extraction, and contrastive theme summarization. Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant process. We enhance the diversity of themes by using structured determinantal point processes for selecting a set of diverse themes with high quality. Finally, we pair contrastive themes and employ an iterative optimization algorithm to select sentences, explicitly considering contrast, relevance, and diversity. Experiments on three datasets demonstrate the effectiveness of our method.

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Cited By

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
Chen STao YYu DLi FGong B(2020)Distribted learning dynamics of Multi-Armed Bandits for edge intelligenceJournal of Systems Architecture10.1016/j.sysarc.2020.101919(101919)Online publication date: Oct-2020
https://doi.org/10.1016/j.sysarc.2020.101919
Le TAkoglu L(2019)ContraVis: Contrastive and Visual Topic Modeling for Comparing Document CollectionsThe World Wide Web Conference10.1145/3308558.3313617(928-938)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313617
Show More Cited By

Index Terms

Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 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 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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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 09 August 2015

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the European Community's Seventh Framework Programme (FP7/2007-2013)
the Yahoo! Faculty Research and Engagement Program
Amsterdam Data Science
the Microsoft Research PhD program
the Netherlands eScience Center
the Netherlands Organisation for Scientific Research (NWO)

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

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

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Cited By

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
Chen STao YYu DLi FGong B(2020)Distribted learning dynamics of Multi-Armed Bandits for edge intelligenceJournal of Systems Architecture10.1016/j.sysarc.2020.101919(101919)Online publication date: Oct-2020
https://doi.org/10.1016/j.sysarc.2020.101919
Le TAkoglu L(2019)ContraVis: Contrastive and Visual Topic Modeling for Comparing Document CollectionsThe World Wide Web Conference10.1145/3308558.3313617(928-938)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313617
Duan YJatowt ACulpepper JMoffat ABennett PLerman K(2019)Across-Time Comparative Summarization of News ArticlesProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291008(735-743)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3291008
Duan YJatowt ATanaka K(2019)Discovering Latent Threads in Entity HistoriesData Science and Engineering10.1007/s41019-019-00108-xOnline publication date: 15-Nov-2019
https://doi.org/10.1007/s41019-019-00108-x
Li LMao LZhang YChi JHuang TCong XPeng H(2018)Computational linguistics literature and citations oriented citation linkage, classification and summarizationInternational Journal on Digital Libraries10.1007/s00799-017-0219-519:2-3(173-190)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s00799-017-0219-5
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
Liang SRen ZZhao YMa JYilmaz ERijke M(2017)Inferring Dynamic User Interests in Streams of Short Texts for User ClusteringACM Transactions on Information Systems10.1145/307260636:1(1-37)Online publication date: 17-Jul-2017
https://dl.acm.org/doi/10.1145/3072606
Song HRen ZLiang SLi PMa Jde Rijke Mde Rijke MShokouhi MTomkins AZhang M(2017)Summarizing Answers in Non-Factoid Community Question-AnsweringProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018704(405-414)Online publication date: 2-Feb-2017
https://dl.acm.org/doi/10.1145/3018661.3018704
Ren ZLiang SLi PWang Sde Rijke Mde Rijke MShokouhi MTomkins AZhang M(2017)Social Collaborative Viewpoint Regression with Explainable RecommendationsProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018686(485-494)Online publication date: 2-Feb-2017
https://dl.acm.org/doi/10.1145/3018661.3018686
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