Abstract
Linguistic summarization provides an explicit, concise and easily understandable description expressed in natural language of huge amount of data for human. The result of extracting linguistic summaries is natural language sentences using given sets of words for each numeric attribute. In general, the word-set is always assumed to be of only \(7 \pm 2\) words represented by fuzzy sets. The interpretability of linguistic summarization depends mainly on this fuzzy set representation of the words. In this paper, we consider the given word-set for each attribute as a linguistic frame of cognition LFoC of this attribute whose size depends only on application requirement. We propose a linguistic summary method based on multi-granularity representations of this LFoCs that can preserve order-based semantics relation and generality-specificity relation of words. Theoretically, the number of words in LFoC are not limited. A simulation study using dataset Iris shows that the proposed method can extract sentences using words of length 3 characterizing dataset Iris that other existing ones cannot do.
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Acknowledgments
This research was supported by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant No 102.01-2017.06; The Vietnam Academy of Science and Technology (VAST) under Grant No VAST01.07/17-18 and the Hanoi National University of Education under No SPHN17-04.
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Pham, T.L., Ho, C.H., Nguyen, C.H. (2018). Linguistic Summarization Based on the Inherent Semantics of Linguistic Words. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_2
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DOI: https://doi.org/10.1007/978-3-319-75429-1_2
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