Type-2 Fuzzy Summarization of Data: An Improved News Generating

Authors:
Adam Niewiadomski
Affiliations:
Institute of Computer Science, Technical University of Lodz, ul. Wólczańska 215, 90-924 Łódź, Poland
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

Citing 3
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Abstract

The paper introduces an improved method of intelligent summarization of large datasets. Previously, the author's solution for automated generating of textual news and comments, based on the standard Yager's method and ordinary fuzzy sets, has been published in [1]. In this paper, a type-2-fuzzy-set-based extension of the concept can be now introduced. Type-2 membership functions are originally applied to build new summarization methods. The approach generalizes the previous methods which are based on traditional fuzzy sets. Moreover, new quality measures of summaries are proposed and used in selecting the optimal and the most specific summaries as the components of textual news. Finally, the method is implemented and evaluated.