Automatic Summarization Of Arabic Texts
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Abstract
This paper presents an automatic summarizer for Arabic texts. The main goal of a summarizer is to produce a condensed version of the content of an input text for various users. The approach exploits mainly the expressions of the language present in the text denoting sentences' relevance according to the author's point of view regardless of the domain. The summarizer selects the most relevant sentences based on linguistic knowledge in the form of linguistic patterns representing language expressions and word lists. Language expressions express the language knowledge and are independent from any specific domain. The paper presents the linguistic acquisition process which feeds the linguistic knowledge base, the architecture of the system including three modules, and the system implementation.
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References
[2] Berri J, Le Roux. D, Malrieu, D, Minel, J.-L., “SERAPHIN main sentences
automatic extraction system”, proceedings of the Second Language Engineering
Convention, London, UK, 1995.
[3] J. Berri, Contribution to the contextual exploration method. Applications to automatic
abstracting and to temporal representations. Implementation of SERAPHIN system.…
Ph.D. thesis, Paris-Sorbonne University (Paris V), France, 1996. (French publication)
[4] H. P. Luhn, “The automatic creation of literature abstracts”. /BM journal for research
and development, 2, 159-165, 1958,
[5] I Mani, Automatic summarization, John Benjamins Publishing Company.
Amsterdam, 2001.
[6] W. Kinsh, T. A. Van Dijk, “Toward a model of text comprehension and production”,
Psychological review, 85, 363-394, 1978.
[7] G. F. DeJong, *An overview of FRUMP system”, Strategies for natural language
processing, Lehnert and Ringle ed., Hillsdale, NJ: Erlbaum Ass., 149-172, 1982.
[8] R. Alterman and L. Bookman, “Some Computational Experiments in
Summarization”, Discourse Processes, 13, 143-174, 1990.
[9] G. Kallgreen, “Automatic abstracting of content in text”, Nordic journal of linguistics,
11,89-110, 1988.
[10] G. SALTON, Automatic Text Processing. The transformation. analysis, and
retrieval of information computer, Addison-Wesley, New York, 1989.
[11] P. Schäuble, “SPIDER : a multiuser information retrieval system for semistructured
and dynamic data”, ACM SIGIR conference on R&D in information retrieval, 318-327,
1993.
[12] S. Miike, E. Itoh, K. Ono, K. Sumita, “A full-text retrieval system with a dynamic
abstract generation function”, Proceedings SIGIR'94, ed. W. Bruce Croft and C. J. van
Rijsbergen, Springer-Verlag, Dublin, 152-161, 1994,
[13] D. Mareu, “Discourse trees are good indicators of importance in texts”, advances in
Automatic Text Summarization, Ed. Inderjeet Mani and Mark T. Maybury, MIT press,
USA, 123-136, 1999.
[14]C. D. Paice, “Constructing literature abstracts by computer: techniques and
prospects”. Information processing management, 26 (1), 171-186, 1990.
[15] Kim, J.-T.. Moldovan I.: “Acquisition of Linguistic Patterns for Knowledge-Based
Information Extraction” /EEE Transactions on Knowledge and Data Engineering, Vol. 7,
No. 5, 713-724, Oct. 1995.
[16] Mädche, A., Neumann, G. Staab S.: 1999. *A Generic Architectural Framework for
Text Knowledge Acquisition”, Unpublished Technical Report, Kalsruhe University, 18p,
1999. Available at htip: www.aifb.uni-kalsruhe.de WBS
[17] J. Berri, M. Al-Khamis, “Information Exploration Using Mobile Agents”, WSEAS
Transactions on Computers, 3 (3), 706-712, 2004.