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| Similarity-Based Estimation for Document Summarization using Fuzzy Sets
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Volume: 1 Issue: 4 |
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Pages: 1-47 |
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Publication
Date: December 2007 |
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ISSN
(Online): 1985-1553 |
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Pages |
1 - 12 |
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Author(s) |
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Published
Date |
30-12-2007 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: fuzzy sets, mass assignment, asymmetric word similarity, topic similarity, summarization |
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| Information is increasing every day and thousands of documents are produced
and made available in the Internet. The amount of information available in
documents exceeds our capacity to read them. We need access to the right
information without having to go through the whole document. Therefore,
documents need to be compressed and produce an overview so that these
documents can be utilized effectively. Thus, we propose a similarity model with
topic similarity using fuzzy sets and probability theories to extract the most
representative sentences. Sentences with high weights are extracted to form a
summary. On average, our model (known as MySum) produces summaries that
are 60% similar to the manually created summaries, while tf.isf algorithm
produces summaries that are 30% similar. Two human summarizers, named P1
and P2, produce summaries that are 70% similar to each other using similar sets
of documents obtained from TREC. |
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K. Sparck Jones. “Automatic Summarizing: Factors and Directions”. In I. Mani and M.T. Maybury, Editors, Advances in Automatic Text Summarization, Cambridge, MA: The MIT Press, pp 1-12, 1999
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S. Yohei ‘‘Sentence Extraction by tf/idf and Position Weighting from Newspaper Articles (TSC-8)’’ NTCIR Workshop 3 Meeting TSC, pp 55-59, 2002
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J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, and A.A. Freitas. “Document Clustering and Text Summarization”. In Proceedings of the 4th Int. Conf. Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), London: The Practical Application Company, pp 41---55, 2000b
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| 1 |
M. S. Binwahlan, N. Salim and L. Suanmalui, “Fuzzy Swarm Diversity Hybrid Model for Text Summarization”, Information Processing & Management, 46(5), pp. 571–588, 2010.
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W. A. Ahmed and S. M. Shamsuddin , “Integration of Least Recently Used Algorithm and Neuro-Fuzzy System into Client-side Web Caching” , International Journal of Computer Science and Security (IJCSS), 3(1), pp. 1 – 15, 2009.
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S. Mansor , R. B. Din and A. Samsudin , “Analysis of Natural Language Steganography”, International Journal of Computer Science and Security (IJCSS), 3(2), pp. 113 – 125, 2009.
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R. Ahmad and A. Khanum , “Document Topic Generation in Text Mining by Using Cluster Analysis with EROCK”, International Journal of Computer Science and Security (IJCSS), 4(2), pp. 176 – 182, 2010.
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