The week's pick
Reseach Article
Analysis of BMW Model for Title Word Selection on Indic Script
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 18 - Number 8 |
| Year of Publication: 2011 |
| Authors: P. Vijayapal Reddy, B. Vishnu Vardhan, A. Govardhan |
10.5120/2304-2915
|
P. Vijayapal Reddy, B. Vishnu Vardhan, A. Govardhan . Analysis of BMW Model for Title Word Selection on Indic Script. International Journal of Computer Applications. 18, 8 ( March 2011), 21-25. DOI=10.5120/2304-2915
Abstract
A title is a short summary that represents document’s main theme. Title can help the reader to have the main idea without reading the entire document. To generate a title for a document, we have to select appropriate words as title words and put them in sequence. The process of generating title for a given document by using machine, can be done by using summarization approaches or by using Statistical approaches or by combing both. For a given document, selecting appropriate words for generating a title by using any available approach mainly depends on the characteristics of the language. In this paper ,we have examined the influence of the language characteristics in the process of title word selection by using the Naïve Bayes probabilistic approach ( called BMW Model ) on the documents which are available in the language ' Telugu '. And also we have investigated the influence of word weight for the selection of title words in BMW Model. By using F1 metric, we have evaluated the title word selection process.
References
- Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries. Michael Witbrock and Vibhu Mittal, Just Research. In Proceedings of SIGIR 99, Berkeley, CA, August 199
- Rong Jin and Alexander G. Hauptmann. Title generation using a training corpus.In CICLing ’01: Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing, pages 208–215, London, UK,2001. Springer-Verlag
- Term-weighting appraoches in automatic text retrieval ,Salton and Buckley Information Processing & Management Vol. 24, No. 5, pp. 513-523,printed in Great Britain. 988
- E. Firmin & M.J. Chrzanowski (1999). An evaluation of automatic text summarization. In I. Mani and M. Maybury, editors. Advances in Automatic Text Summarization. MIT Press, Cambridge, Massachusetts, 1999
- C. H. Leung & W.K. Kan (1997). A statistical learning approach to automatic indexing of controlled index terms. Journal of the American Society for Information Science, 48 (1), 55-66, 1997.
- P.D. Turney (2000). Learning algorithms for keyphrase extraction. Information Retrieval, 2(4): 303-336, 2000
- I. Mani & M. Maybury (1999). Advances in Automated Text Summarization.Cambridge, MA: MIT Press, 1999
- K. S. Jones & P. Willett (1997). Reading in Information Retrieval. Morgan Kaufmann Publishers, 1997
- MUC-6 (1995), Proceeding of The Sixth Message Understanding Conference, 1995
- Padmaja Rani B., Vishnu Vardhan B., Kanaka Durga A., Govardhan A., Pratap Reddy L., and Vinaya Babu A. Telugu Document Classification using Baye’s Probabilistic Model Technology spectrum, Journal of JNTU, vol.2 No.1, 2008, pp.26- 30
- M. Banko, V. Mittal, and M. Witbrock. Headline generation based on statistical translation. In the Proceedings of Association for Computational Linguistics, 2000.
- V. Rjiesbergen (1979). Information Retrieval. Chapter 7. Butterworths, London, 1979.
- Statistical Approaches toward title generation by Rong Jin , 2003, Ph.D Thesis
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