Abstract
This paper describes a sentence ranking technique using entropy measures, in a multi-document unstructured text summarization application. The method is topic specific and makes use of a simple language independent training framework to calculate entropies of symbol units. The document set is summarized by assigning entropy-based scores to a reduced set of sentences obtained using a graph representation for sentence similarity. The performance is seen to be better than some of the common statistical techniques, when applied on the same data set. Commonly used measures like precision, recall and f-score have been modified and used as a new set of measures for comparing the performance of summarizers. The rationale behind such a modification is also presented. Experimental results are presented to illustrate the relevance of this method in cases where it is difficult to have language specific dictionaries, translators and document-summary pairs for training.
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References
Baldwin, B., Morton, T.: Dynamic Co-Reference Based Summarization. In: Proc. Third Conference on Emperical Methods in Natural Language Processing, pp. 630–632 (1998)
Carbonell, J.G., Goldstein, J.: Use of mmr Diversity-Based Re-Ranking for Recording Documents and Producing Summaries. In: Proc. ACM, SIGIR 1998 (1998)
Hovy, E.H., Lin, C.Y.: Automated Text Summarization in SUMMARIST, ch. 8. MIT Press, Cambridge (1999)
Deerwester, S.D., et al.: Indexing by Latent Semantic Analysis. American Society for Information Science 41, 391–407 (1990)
Paice, C.: Constructing Literature Abstracts by Computer: Techniques and Prospects. Information Processing and Management 26, 171–186 (1990)
Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Proc. Workshop on Intelligent Scalable Text Summarization, Madrid, Spain (1997)
Morris, J., Hirst, G.: Lexical Cohesion Computed by Thesaural Relations as an Indication of the Structure of Text. Computational Linguistics 17, 21–43 (1991)
Yihong Gong, X.L.: Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. In: Proc. ACM SIGIR 2001, pp. 19–25 (2001)
Radev, D., Budzikowska, M: Centroid-Based Summarization of Multiple Documents: Sentence Extaction, Utility-Based Evaluation and User Studies. In: Proc. ANLP/NAACL 2000 (2000)
Dragomir Radev, V.H., McKeowen, K.R.: A Description of the Cidr System as Used for tdt-2. In: Proc. DARPA Broadcast News Workshop, Herndon (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Ravindra, G., Balakrishnan, N., Ramakrishnan, K.R. (2004). Multi-document Automatic Text Summarization Using Entropy Estimates. In: Van Emde Boas, P., Pokorný, J., Bieliková, M., Štuller, J. (eds) SOFSEM 2004: Theory and Practice of Computer Science. SOFSEM 2004. Lecture Notes in Computer Science, vol 2932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24618-3_25
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DOI: https://doi.org/10.1007/978-3-540-24618-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20779-5
Online ISBN: 978-3-540-24618-3
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