research-article

Comparative Document Summarization via Discriminative Sentence Selection

Authors:

Yihong GongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 7, Issue 1

Article No.: 2, Pages 1 - 18

https://doi.org/10.1145/2435209.2435211

Published: 01 March 2013 Publication History

Abstract

Given a collection of document groups, a natural question is to identify the differences among these groups. Although traditional document summarization techniques can summarize the content of the document groups one by one, there exists a great necessity to generate a summary of the differences among the document groups. In this article, we study a novel problem of summarizing the differences between document groups. A discriminative sentence selection method is proposed to extract the most discriminative sentences that represent the specific characteristics of each document group. Experiments and case studies on real-world data sets demonstrate the effectiveness of our proposed method.

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Guo YWang FXing CLu X(2022)Mining multi-brand characteristics from online reviews for competitive analysisElectronic Commerce Research and Applications10.1016/j.elerap.2022.10114153:COnline publication date: 1-May-2022
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Wang YWu JChou TLin YKao H(2020)Enhance Content Selection for Multi-Document Summarization with Entailment Relation2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI51410.2020.00030(119-124)Online publication date: Dec-2020
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Index Terms

Comparative Document Summarization via Discriminative Sentence Selection
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 7, Issue 1

March 2013

122 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2435209

Issue’s Table of Contents

Copyright © 2013 ACM.

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Publication History

Published: 01 March 2013

Accepted: 01 April 2012

Revised: 01 April 2012

Received: 01 June 2011

Published in TKDD Volume 7, Issue 1

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Guo YWang FXing CLu X(2022)Mining multi-brand characteristics from online reviews for competitive analysisElectronic Commerce Research and Applications10.1016/j.elerap.2022.10114153:COnline publication date: 1-May-2022
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Wang YWu JChou TLin YKao H(2020)Enhance Content Selection for Multi-Document Summarization with Entailment Relation2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI51410.2020.00030(119-124)Online publication date: Dec-2020
https://doi.org/10.1109/TAAI51410.2020.00030
Wu HSun MMi PTatti NNorth CRamakrishnan N(2018)Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy ModelsACM Transactions on Knowledge Discovery from Data10.1145/304701712:1(1-34)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1145/3047017
Wu DLu LHussain MLi SLi MZhang F(2017)R3ACM Transactions on Embedded Computing Systems10.1145/307072017:1(1-25)Online publication date: 20-Sep-2017
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Su ZZeng KLi HLuo X(2017)A Dual-Domain Perceptual Framework for Generating Visual Inconspicuous CounterpartsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/306842713:2(1-21)Online publication date: 26-Apr-2017
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Ren XLv YWang KHan Jde Rijke MShokouhi MTomkins AZhang M(2017)Comparative Document Analysis for Large Text CorporaProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018690(325-334)Online publication date: 2-Feb-2017
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