research-article

Content coverage maximization on word networks for hierarchical topic summarization

Authors:

Chengxiang Zhai,

Jiawei HanAuthors Info & Claims

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

Pages 249 - 258

https://doi.org/10.1145/2505515.2505585

Published: 27 October 2013 Publication History

Abstract

This paper studies text summarization by extracting hierarchical topics from a given collection of documents. We propose a new approach of text modeling via network analysis. We convert documents into a word influence network, and find the words summarizing the major topics with an efficient influence maximization algorithm. Besides, the influence capability of the topic words on other words in the network reveal the relations among the topic words. Then we cluster the words and build hierarchies for the topics. Experiments on large collections of Web documents show that a simple method based on the influence analysis is effective, compared with existing generative topic modeling and random walk based ranking.

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Cited By

Zhang CHan J(2022)Multidimensional Mining of Massive Text DataundefinedOnline publication date: 19-Mar-2022
Vardasbi AFaili HAsadpour M(2019)Solving submodular text processing problems using influence graphsSocial Network Analysis and Mining10.1007/s13278-019-0559-99:1Online publication date: 7-May-2019
Liu QWang ZChen E(2018)Influence Maximization ModelEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110197(1075-1082)Online publication date: 12-Jun-2018
Show More Cited By

Index Terms

Content coverage maximization on word networks for hierarchical topic summarization
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information systems applications
    1. Data mining

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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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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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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

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

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Citations

Cited By

Zhang CHan J(2022)Multidimensional Mining of Massive Text DataundefinedOnline publication date: 19-Mar-2022
Vardasbi AFaili HAsadpour M(2019)Solving submodular text processing problems using influence graphsSocial Network Analysis and Mining10.1007/s13278-019-0559-99:1Online publication date: 7-May-2019
Liu QWang ZChen E(2018)Influence Maximization ModelEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110197(1075-1082)Online publication date: 12-Jun-2018
Chen YPerozzi BSkiena S(2017)Vector-based similarity measurements for historical figuresInformation Systems10.1016/j.is.2016.07.00164:C(163-174)Online publication date: 1-Mar-2017
Ramachandran LGehringer EYadav R(2017)Automated Assessment of the Quality of Peer Reviews using Natural Language Processing TechniquesInternational Journal of Artificial Intelligence in Education10.1007/s40593-016-0132-x27:3(534-581)Online publication date: 11-Jan-2017
Song LZhang PBao ZSellis T(2017)Continuous Summarization over Microblog ThreadsDatabase Systems for Advanced Applications10.1007/978-3-319-55699-4_31(511-526)Online publication date: 22-Mar-2017
Liu QWang ZChen E(2017)Influence Maximization ModelEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110197-1(1-8)Online publication date: 18-Aug-2017
Chen WLin TYang C(2016)Real-time topic-aware influence maximization using preprocessingComputational Social Networks10.1186/s40649-016-0033-z3:1Online publication date: 10-Nov-2016
Ye JMing ZChua T(2016)Generating Incremental Length Summary Based on Hierarchical Topic Coverage MaximizationACM Transactions on Intelligent Systems and Technology10.1145/28094337:3(1-33)Online publication date: 17-Feb-2016
Wang CLiu XSong YHan JCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Towards Interactive Construction of Topical HierarchyProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783288(1225-1234)Online publication date: 10-Aug-2015
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