research-article

Query-based Multi-Document Summarization by Clustering of Documents

Authors:

Gopal K. R. Naveen,

Prema NedungadiAuthors Info & Claims

ICONIAAC '14: Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing

Article No.: 58, Pages 1 - 8

https://doi.org/10.1145/2660859.2660972

Published: 10 October 2014 Publication History

Get Access

Abstract

Information Retrieval (IR) systems such as search engines retrieve a large set of documents, images and videos in response to a user query. Computational methods such as Automatic Text Summarization (ATS) reduce this information load enabling users to find information quickly without reading the original text. The challenges to ATS include both the time complexity and the accuracy of summarization. Our proposed Information Retrieval system consists of three different phases: Retrieval phase, Clustering phase and Summarization phase. In the Clustering phase, we extend the Potential-based Hierarchical Agglomerative (PHA) clustering method to a hybrid PHA-ClusteringGain-K-Means clustering approach. Our studies using the DUC 2002 dataset show an increase in both the efficiency and accuracy of clusters when compared to both the conventional Hierarchical Agglomerative Clustering (HAC) algorithm and PHA.

References

[1]

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008, Introduction to Information Retrieval, Cambridge University Press, New York, NY, USA.

Digital Library

Google Scholar

[2]

Ricardo A. Baeza-Yates and Berthier Ribeiro-Neto. 1999, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

Digital Library

Google Scholar

[3]

David A. Grossman and Ophir Frieder. 1998, Information Retrieval: Algorithms and Heuristics, Kluwer Academic Publishers, Norwell, MA, USA.

Digital Library

Google Scholar

[4]

Daniel M. Dunlavy, Dianne P. O Leary, John M. Conroy, Judith D. Schlesinger, QCS: A system for querying, clustering and summarizing documents, Information Processing and Management, ScienceDirect, 2007. Volume 43, Issue 6, November 2007, Pages 1588--1605.

Digital Library

Google Scholar

[5]

Yonggang Lu, Yi Wan, PHA: A fast potential-based hierarchical agglomerative clustering method, Pattern Recognition, ScienceDirect, 2013, Volume 46, Issue 5, May 2013, Pages 1227--1239.

Digital Library

Google Scholar

[6]

Rada Mihalcea. 2004, Graph-based ranking algorithms for sentence extraction, applied to text summarization, In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (ACLdemo '04), Association for Computational Linguistics, Stroudsburg, PA, USA, Article 20.

Digital Library

Google Scholar

[7]

Yunjae Jung, Haesun Park, A Decision Criteria for the Optimal Number of Clusters in Hierarchical Clustering, 2002, Kluwer Academic Publishers.

Google Scholar

[8]

Anastasios Tombros, Robert Villa, & C. J. Van Rijsbergen, The effectiveness of query-specific hierarchic clustering in information retrieval, Information Processing and Management, ScienceDirect, 2002, Volume 38, Issue 4, July 2002, Pages 559--582.

Digital Library

Google Scholar

[9]

Velmurugan T., Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data, Applied Soft Computing, ScienceDirect, 2014, Volume 19, June 2014, Pages 134--146.

Google Scholar

[10]

DUC, 2002, Document Understanding Conference (DUC), 2002, http://wwwnlpir.nist.gov/projects/duc/guidelines/2002.html.

Google Scholar

[11]

Lin, Chin-Yew. 2004a, ROUGE: a Package for Automatic Evaluation of Summaries, In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25 - 26, 2004.

Google Scholar

[12]

Ju-Hong Lee, Sun Park, Chan-Min Ahn, & Daeho Kim, Automatic generic document summarization based on non-negative matrix factorization, Information Processing and Management, ScienceDirect, 2009, Volume 45, Issue 1, January 2009, Pages 20--34.

Digital Library

Google Scholar

[13]

A. K. Jain, M. N. Murty, and P. J. Flynn. 1999, Data clustering: a review, ACM Comput. Surv. 31, 3 (September 1999), 264--323.

Digital Library

Google Scholar

[14]

Lin, Chin-Yew and E.H. Hovy 2003, Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics, In Proceedings of 2003 Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada, May 27 - June 1, 2003.

Digital Library

Google Scholar

Cited By

View all

Ramachandran RK AJ K(2024)Knowledge Extraction from Distributed Heterogeneous Data Sources2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10592904(1-6)Online publication date: 24-May-2024
https://doi.org/10.1109/INCET61516.2024.10592904
Roy PKundu S(2023)Review on Query-focused Multi-document Summarization (QMDS) with Comparative AnalysisACM Computing Surveys10.1145/359729956:1(1-38)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3597299
Palanisamy DNarayanan J(2023)Network Analysis of Research Base Papers: Metrics and Potential use2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307301(1-9)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10307301
Show More Cited By

Index Terms

Query-based Multi-Document Summarization by Clustering of Documents
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
  2. Information systems applications
    1. Data mining

Recommendations

Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering
Abstract
Fast centroid-based clustering algorithms such as k-means usually converge to a local optimum. In this work, we propose a method for constructing a better clustering from two such suboptimal clustering solutions based on the fact that each ...
Using Bisect K-Means Clustering Technique in the Analysis of Arabic Documents

In this article, I have investigated the performance of the bisect K-means clustering algorithm compared to the standard K-means algorithm in the analysis of Arabic documents. The experiments included five commonly used similarity and distance functions ...
Text document clustering based on neighbors

Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of k-means, are reported performing well on document clustering. They treat the clustering problem as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICONIAAC '14: Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing

October 2014

374 pages

ISBN:9781450329088

DOI:10.1145/2660859

General Chair:
P. Venkat Rangan,
Program Chair:
Bharat Jayaraman

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 ACM 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]

In-Cooperation

Amrita: Amrita Vishwa Vidyapeetham

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICONIAAC '14

ICONIAAC '14: International Conference on Interdisciplinary Advances in Applied Computing

October 10 - 11, 2014

Amritapuri, India

Acceptance Rates

ICONIAAC '14 Paper Acceptance Rate 69 of 176 submissions, 39%;

Overall Acceptance Rate 69 of 176 submissions, 39%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)3

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ramachandran RK AJ K(2024)Knowledge Extraction from Distributed Heterogeneous Data Sources2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10592904(1-6)Online publication date: 24-May-2024
https://doi.org/10.1109/INCET61516.2024.10592904
Roy PKundu S(2023)Review on Query-focused Multi-document Summarization (QMDS) with Comparative AnalysisACM Computing Surveys10.1145/359729956:1(1-38)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3597299
Palanisamy DNarayanan J(2023)Network Analysis of Research Base Papers: Metrics and Potential use2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307301(1-9)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10307301
Belwal RRai SGupta A(2022)Extractive text summarization using clustering-based topic modelingSoft Computing10.1007/s00500-022-07534-627:7(3965-3982)Online publication date: 4-Oct-2022
https://doi.org/10.1007/s00500-022-07534-6
Lal NKrishnanunni SVijayakumar VVaishnavi NSiji Rani SDeepa Raj K(2021)A Novel Approach to Text Summarisation Using Topic Modelling and Noun Phrase ExtractionAdvances in Computing and Network Communications10.1007/978-981-33-6987-0_24(285-298)Online publication date: 13-Jun-2021
https://doi.org/10.1007/978-981-33-6987-0_24
Madhuri Chandu GPremkumar AK SSampath N(2019)Extractive Approach For Query Based Text Summarization2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)10.1109/ICICT46931.2019.8977708(1-5)Online publication date: Sep-2019
https://doi.org/10.1109/ICICT46931.2019.8977708
Aravind MViswanath SMohan NAdarsh RBhaskar J(2019)A Modified Medical Information Retrieval System2019 IEEE 9th International Conference on Advanced Computing (IACC)10.1109/IACC48062.2019.8971587(218-222)Online publication date: Dec-2019
https://doi.org/10.1109/IACC48062.2019.8971587
Raj K. DSajeev G(2018)A Community Based Web Summarization in Near Linear Time2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2018.8554789(962-968)Online publication date: Sep-2018
https://doi.org/10.1109/ICACCI.2018.8554789
Beck TBöschen FScherp A(2018)What to Read Next? Challenges and Preliminary Results in Selecting Representative DocumentsDatabase and Expert Systems Applications10.1007/978-3-319-99133-7_19(230-242)Online publication date: 7-Aug-2018
https://doi.org/10.1007/978-3-319-99133-7_19

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering

Using Bisect K-Means Clustering Technique in the Analysis of Arabic Documents

Text document clustering based on neighbors