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Similarity-Based Estimation for Document Summarization using Fuzzy Sets
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International Journal of Computer Science and Security (IJCSS)
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Volume:  1    Issue:  4
Pages:  1-47
Publication Date:   December 2007
ISSN (Online): 1985-1553
1 - 12
Published Date   
CSC Journals, Kuala Lumpur, Malaysia
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
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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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.
2 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.
3 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.
4 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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Masrah Azrifah Azmi Murad : Colleagues
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