EXTRACTION-BASED TEXT SUMMARIZATION USING FUZZY ANALYSIS

Document Type : Research Paper

Authors

1 Islamic Azad University of Shahrekord branch, Shahrekord, Iran

2 Shahid Bahonar University of Kerman, International Center for Science and High Technology and Environmental Sciences, Kerman, Iran

3 Shahid Bahonar University of Kerman, The centre of Excellence for Fuzzy system and applications, Kerman, Iran

4 Department of Energy, Electrical Engineering division, Politecnico di Milano, Milan, Italy

Abstract

Due to the explosive growth of the world-wide web, automatic
text summarization has become an essential tool for web users. In this paper
we present a novel approach for creating text summaries. Using fuzzy logic
and word-net, our model extracts the most relevant sentences from an original
document. The approach utilizes fuzzy measures and inference on the
extracted textual information from the document to find the most significant
sentences. Experimental results reveal that the proposed approach extracts
the most relevant sentences when compared to other commercially available
text summarizers. Text pre-processing based on word-net and fuzzy analysis
is the main part of our work.

Keywords

References

[1] P. B. Baxendale, Machine made index for technical literature: an experiment, IBM Journal
of Research and Development, 2(4) (1958), 354-361.
[2] R. Brandow, K. Mitlze and L. Rau, Automatic condensation of electronic Publication by
sentence election, Information Processing and Management, 31(5) (1995), 675-685.
[3] J. J. Buckley, K. D. Reilly and L. J. Jowers, Simulating continuous fuzzy systems: I, Iranian
Journal of Fuzzy Systems, 2(1) (2005), 1-18.
[4] W. T. Chuang and J. Yang, Extracting sentences segments for text summarization: a machine
learning approaches, Proceedings of the 23th Annual International ACM SIGIR Conference
on Research and Development in Information Retrieval, Athens, Greece, (2000), 125-159.
[5] N. Elhadad, User-sensitive text summarization thesis summary, Thesis Summary, American
Association for Artificial Intelligence, USA, 2004.
[6] Y. Gong and X. Liu, Creating generic text summaries, IEEE, 0-7695-1263-1/01, (2001), 391-
407.
[7] K. Kaikhah, Automatic text summarization with NNs, Second IEEE International Conference
on Intelligent Systems, June (2004), 40-44.
[8] A. Kiani-B, M. R. Akbarzadeh-T and M. H. Moeinzadeh, Intelligent extractive text summarization
using fuzzy inference systems, 1-4244-0457-6/06, IEEE, (2001), 1-4.
[9] J. Kupiec, J. Pederson and F. Chen, A trainable document summarizer, Proceedings of
the 18th Annual international ACM SIGIR Confluence on Research and Development in
Information Retrieval, Seattle, Washington, (1995), 68-73.
[10] J. Leskovec, M. Grobelnik and N. Milic-Frayling, Learning semantic graph mapping for document
summarization, Proceedings of ECML/PKDD-2004 Workshop on Knowledge Discovery
and Ontologies, KDO-2004, Pisa, Italy.
[11] C. Y. Lin, ROUGE: a package for automatic evaluation of summaries, Proceedings of Workshop
on Text Summarization Branches Out, Post-conference Workshop of ACL, Spain, 2004.
[12] C. Y. Lin and E. Hovy, Automatic evaluation of summaries using n-gram co-occurrence
statistics, Proceedings of the Human Technology conference (HLT-NAACL-2003), Canada,
(2003), 71-78.
[13] C. Y. Lin and E. H. Hovy, Automatic evaluation of summaries using n-gram co-occurrence
statistics, Proceedings of Language Technology Conference (HLT-NAACL 2003), Edmonton,
Canada, (2003), 287-292.
[14] I. Mani, Advances in automatic summarization, John Benjamins Publishing Company,
(2001), 129-165.
[15] E. G. Mansoori, M. J. Zolghadri and S. D. Katebi, Using distribution of data to enhance
performance of fuzzy classification systems, Iranian Journal of Fuzzy Systems, 4(1) (2007),
22-36.
[16] G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller, Five papers on wordnet,
Technical Report, Princeton University, (1993), 3-12.
[17] T. Nomoto and Y. Matsumoto, A new approach to unsupervised text summarization, SIGIR,
ACM, New Orleans, Louisiana, USA, (2001), 26-34.
[18] P. Over and J. Yen, An introduction to duc 2003 - intrinsic evaluation of generic news
text summarization systems, http:// wwwnlpir.nist.gov/ projects/ duc/ pubs/ 2003slides/
duc2003intro.pdf, 2003.
[19] K. Papineni, S. Roukos, T. Ward and W. J. Zhu, BLEU: A method for automatic evaluation
of machine translation, IBM Research Report RC22176 (W0109-022), 2001.
[20] H. Saggion, D. Radev, S. Teufel and W. Lam, Meta-evaluation of summaries in a crosslingual
environment using content-based metrics, Proceedings of COLING, Taipei, Taiwan,
2002.
[21] A. K. Shaymal and M. Pal, Triangular fuzzy matrices, Iranian Journal of Fuzzy Systems,
4(1) (2007), 75-87.
[22] L. X. Wang, A cource in fuzzy system and control, Prentice Hall, Englewood Cliffs, Nj.
ISBN-13: 978-01354088271998.
[23] C. C. Yang and F. L. Wang, Fractal summarization: summarization based on fractal theory,
SIGIR, ACM 1-58113-646, Toronto, CA, (2003), 391-392.
[24] C. C. Yang and F. L. Wang, Hierarchical summarization of large documents, Journal of the
American Society for Information Science and Technology, 59(6) (2008), 887-902.
[25] L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, Elsevier,
Holland, (1999), 9-34.
Iranian Journal of Fuzzy Systems
Volume 7, Issue 3 - Serial Number 3
September and October 2010
Pages 15-32

Files

Share

How to cite

Statistics