Abstract
In this paper, we present a method to generate an extractive summary from a single document using subjective logic. The idea behind our approach is to consider words and their co-occurrences between sentences in a document as evidence of their relatedness to the contextual meaning of the document. Our aim is to formulate a measure to find out ‘opinion’ about a proposition (which is a sentence in this case) using subjective logic in a closed environment (as in a document). Stronger opinion about a sentence represents its importance and are hence considered to summarize a document. Summaries generated by our method when evaluated with human generated summaries, show that they are more similar than baseline summaries.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
DUC (2001), The document understanding conference, http://duc.nist.gov/
Dalianis, H.: SweSum-A Text Summarizer for Swedish (2000), http://www.dsv.su.se/%7Ehercules/papers.Textsumsummary.html
Jøsang, A.: Artificial reasoning with subjective logic. In: Proceedings of the Second Australian Workshop on Commonsense Reasoning, vol. 48 (1997) Perth:[sn]
Jøsang, A.: A logic for uncertain probabilities. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(3), 279–311 (2001)
Liddy, E.D.: The discourse-level structure of empirical abstracts: an exploratory study. Inf. Process. Manage. 27(1), 55–81 (1991)
Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries, Barcelona, Spain, July 2004, pp. 74–81. Association for Computational Linguistics (2004)
Lin, C.Y., Hovy, E.: Identifying topics by position. In: Proceedings of the fifth conference on Applied natural language processing, pp. 283–290. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Lin, C.Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, p. 78. Association for Computational Linguistics (2003)
Mani, I., Maybury, M.T.: Advances in automatic text summarization. MIT Press, Cambridge (1999)
Pardo, T.A.S., Rino, L.H.M., Nunes, M.G.V.: Extractive summarization: how to identify the gist of a text. In: The Proceedings of the 1st International Information Technology Symposium–I2TS, Citeseer, pp. 1–6 (2002)
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
Manna, S., Mendis, B.S.U., Gedeon, T. (2010). Capture of Evidence for Summarization: An Application of Enhanced Subjective Logic. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-13672-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
Online ISBN: 978-3-642-13672-6
eBook Packages: Computer ScienceComputer Science (R0)