research-article

Social context summarization

Authors:

Juanzi LiAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 255 - 264

https://doi.org/10.1145/2009916.2009954

Published: 24 July 2011 Publication History

Abstract

We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.

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Cited By

Kumar RChakraborty RTiwari ASaha SSaini NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment SensitivityProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614849(1117-1126)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614849
Nguyen MTran DPhan VNguyen L(2023)Towards Social Context Summarization with Convolutional Neural NetworksComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_27(341-353)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_27
Huang YYu ZXiang YYu ZGuo J(2022)Exploiting comments information to improve legal public opinion news abstractive summarizationFrontiers of Computer Science10.1007/s11704-021-0561-z16:6Online publication date: 22-Jan-2022
https://doi.org/10.1007/s11704-021-0561-z
Show More Cited By

Index Terms

Social context summarization
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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

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Cited By

Kumar RChakraborty RTiwari ASaha SSaini NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment SensitivityProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614849(1117-1126)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614849
Nguyen MTran DPhan VNguyen L(2023)Towards Social Context Summarization with Convolutional Neural NetworksComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_27(341-353)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_27
Huang YYu ZXiang YYu ZGuo J(2022)Exploiting comments information to improve legal public opinion news abstractive summarizationFrontiers of Computer Science10.1007/s11704-021-0561-z16:6Online publication date: 22-Jan-2022
https://doi.org/10.1007/s11704-021-0561-z
Jalil ZNasir JNasir M(2021)Extractive Multi-Document Summarization: A Review of Progress in the Last DecadeIEEE Access10.1109/ACCESS.2021.31124969(130928-130946)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3112496
Chaghari AFeizi‐Derakhshi MBalafar M(2020)The combination of term relations analysis and weighted frequent itemset model for multidocument summarizationComputational Intelligence10.1111/coin.1227036:2(783-812)Online publication date: 29-Jan-2020
https://doi.org/10.1111/coin.12270
Nguyen MNguyen VVu HNguyen V(2020)Transformer-based Summarization by Exploiting Social Information2020 12th International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE50997.2020.9287388(25-30)Online publication date: 12-Nov-2020
https://doi.org/10.1109/KSE50997.2020.9287388
Nguyen MCuong THoai N(2019)Exploiting User Comments for Document Summarization with Matrix FactorizationProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369699(118-124)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3368926.3369699
Qiang JChen PDing WWang TXie FWu X(2019)Heterogeneous-Length Text Topic Modeling for Reader-Aware Multi-Document SummarizationACM Transactions on Knowledge Discovery from Data10.1145/333303013:4(1-21)Online publication date: 8-Aug-2019
https://dl.acm.org/doi/10.1145/3333030
Cagliero LGarza PBaralis E(2019)ELSAACM Transactions on Information Systems10.1145/329898737:2(1-33)Online publication date: 16-Jan-2019
https://dl.acm.org/doi/10.1145/3298987
Liu HWang LZhao PWu X(2019)Document Specific Supervised Keyphrase Extraction With Strong Semantic RelationsIEEE Access10.1109/ACCESS.2019.29488917(167507-167520)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2948891
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