Abstract
In this paper we propose a summarization approach, nicknamed AZOM, that combines statistical and conceptual property of text and in regards of document structure, extracts the summary of text. AZOM is also capable of summarizing unstructured documents. Proposed approach is localized for Persian language but easily can apply to other languages. The empirical results show comparatively superior results than common structured text summarizers, also than existing Persian text summarizers.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Luhn, H.P.: The Automatic Creation of Literature Abstract. IBM Journal of Research and Development 2, 159–165 (1958)
Yatsko, V.A.: Special Features of the Communication Syntatical Structure of Summary Utterances. NTI 2, 1–5 (1993)
Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, Spain, pp. 10–17 (1997)
Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: A Theory of Text Organization (1987)
McCarty, L.T.: Deep Semantic Interpretations Of Legal Texts. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, USA, pp. 217–224 (2007)
Reynar, J.C., Ratnaparkhi, A., Maximum, A.: Entropy Approach to Identifying Sentence Boundaries. In: Proceedings of the 5th Conference on Applied Natural Language Processing, pp. 16–19 (1997)
Kashefi, O., Mohseni, N., Minaei, B.: Optimizing Document Similarity Detection in Persian Information Retrieval. Journal of Convergence Information Technology 5, 101–106 (2010)
Yang, C.C., Wang, F.L.: Hierarchical Summarization of Large Document. American Society or Information Science and Tecnology 10, 888–902 (2008)
Zamanifar, A., Minaei-Bidgoli, B., Sharifi, M.: A New Hybrid Farsi Text Summarization Technique Based on Term Co-Occurrence and Conceptual Property of the Text. In: 9th SNPD Conference, pp. 635–639. IEEE Computer Society, Thiland (2008)
Zamanifar, A., Minaei, B., Kashefi, O.: A New Technique for Detecting Similar Documents based on Term Co-occurrence and Conceptual Property of the Text. In: Int. Conf. on Digital Information Management, England, pp. 526–531 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zamanifar, A., Kashefi, O. (2011). AZOM: A Persian Structured Text Summarizer. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-22327-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22326-6
Online ISBN: 978-3-642-22327-3
eBook Packages: Computer ScienceComputer Science (R0)