Abstract
We present in this paper an automatic summarization method of Arabic documents. This method is based on a numerical approach which uses a semi-supervised learning technique. The proposed method consists of two phases. The first one is the learning phase and the second is the use phase. The learning phase is based on the Support Vector Machine (SVM) algorithm. In order to evaluate our method, we conducted a comparative study that involves the results generated by our system AIS (Arabic Intelligent Summarizer) with that realized by a human expert. The obtained results are very encouraging and we plan to extend our evaluation on a larger corpus to ensure the performance of our system.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
MaÃloul, M.H., Ellouze Khemakhem, M., Belguith Hadrich, L.: Al Lakas El’eli /ÇááÎÇÕ ÇáÂáí: Un système de résumé automatique de documents arabes. International Business Information Management Association (IBIMA 2008) (2008)
Amini, M.R., Gallinari, P.: Apprentissage numérique pour le résumé de texte. Les Journées d’Étude de l’ATALA, Le résumé de texte automatique: solutions et perspectives (2003)
Douzidia, S., Lapalme, G.: Lakhas, an Arabic summarization system. In: Proceedings of the HLT-NAACL Workshop on Text Summarization DUC 2004 (2004)
Maâloul, M.H., Ellouze Khemakhem, M., Belguith Hadrich, L.: Proposition d’une méthode de résumé automatique de documents arabes. GEI 2006 (2006)
Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th ACM SIGIR Conference (1995)
Luhn, H.P.: The automatic creation of literature abstracts. IBM Journal of Research and Development (1958)
Alrahabi, M., Mourad, G., Djioua, B.: Filtrage sémantique de textes en arabe en vue d’un prototype de résumé automatique. In: JEP/TALN 2004 (2004)
Mani, I., Bloedorn, E.: Machine Learning of Generic and User-Focused Summarization. In: Proceedings of the Fifteenth National Conference of Artificial Intelligence, AAAI 1998 (1998)
Amini, M.R.: Apprentissage automatique et recherche de l’information: application à l’extraction d’information de surface et au résumé de texte. Thèse de doctorat (2001)
Amini, M.R., Gallinari, P.: The Use of Unlabeled Data to Improve Supervised Learning for Text Summarization. In: SIGIR (2002)
Bossard, A., Généreux, M., Poibeau, T.: CBSEAS, a Summarization System Integration of Opinion Mining Techniques to Summarize Blogs (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boudabous, M.M., Maaloul, M.H., Belguith, L.H. (2010). Digital Learning for Summarizing Arabic Documents. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds) Advances in Natural Language Processing. NLP 2010. Lecture Notes in Computer Science(), vol 6233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14770-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-14770-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14769-2
Online ISBN: 978-3-642-14770-8
eBook Packages: Computer ScienceComputer Science (R0)