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Generating Multi-Document Summarization using Data Merging Technique

by G.V. Garje, S.V. Khaladkar, A.N. Khengare, J.M. Pawar, M.S. Vidhate
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 6
Year of Publication: 2016
Authors: G.V. Garje, S.V. Khaladkar, A.N. Khengare, J.M. Pawar, M.S. Vidhate
10.5120/ijca2016908847

G.V. Garje, S.V. Khaladkar, A.N. Khengare, J.M. Pawar, M.S. Vidhate . Generating Multi-Document Summarization using Data Merging Technique. International Journal of Computer Applications. 138, 6 ( March 2016), 6-8. DOI=10.5120/ijca2016908847

@article{ 10.5120/ijca2016908847,
author = { G.V. Garje, S.V. Khaladkar, A.N. Khengare, J.M. Pawar, M.S. Vidhate },
title = { Generating Multi-Document Summarization using Data Merging Technique },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 6 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number6/24381-2016908847/ },
doi = { 10.5120/ijca2016908847 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:55.352739+05:30
%A G.V. Garje
%A S.V. Khaladkar
%A A.N. Khengare
%A J.M. Pawar
%A M.S. Vidhate
%T Generating Multi-Document Summarization using Data Merging Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 6
%P 6-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we propose a summarization method to summarize a set of co-referent documents that has been clustered using hard clustering techniques. The main focus of this paper lies on the weighted optimal merge function (fβ). This weighted optimal merge function uses the weighted harmonic mean to find the balance between precision and recall. The global precision and recall measures are defined by triangular norm. Triangular norm receives local precision and recall values as input, in order to generate a multi-set of key concepts that we use to generate summarization.

References
  1. D.Van Britson, A. Bronselaer and G. De. Tre, (2015) “Using data merging techniques for generating multi-document summarizations”, in IEEE Transactions on Fuzzy System, Vol - 23, NO.3, pp. 576 – 592.
  2. Jade Goldstein, Vibhu Mittal, Jaime Carbonell, Mark Kantrowitz, (2000) “Multi Document Summarization by Sentence Extraction”, in proceedings of NAACL-ANLP Workshop on Automatic summarization, Volume 4, pp.40-48.
  3. Jun ichi Fukumoto, (2004) “Multi-Summarization using document set type classification”, in Proceedings of NTCIR- 4, Tokyo.
  4. Judith D. Schlesinger, Dianne P. O’Leary, and John M. Conroy, (2008) “Arabic/English Multi-document Summarization with CLASSY-The Past and the Future” in the proceedings of Computational Linguistics and Intelligent Text Processing, 9th International Conference, Haifa, Israel, Springer, pp 568–581.
  5. Pallavi D. Patil and N. J. Kulkarni, (2014) “Text Summarization using Fuzzy-Logic”, in International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 3.
  6. Fatma El-Ghanna and Tarek El-Shishtawy. (2013) “Multi-Topic Multi-Document Summarizer” in International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 6.
Index Terms

Computer Science
Information Sciences

Keywords

Content Selection Weighted optimal merge function Multi-document summarization.

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