Fuzzy Rough Set-Based Sentence Similarity Measure and its Application to Text Summarization

摘要

Fuzzy Rough Sets are designed for decision-making with uncertainty, imprecision, and incompleteness in data. We propose to use Fuzzy Rough Sets for the task of sentence similarity-based Text Summarization. Text data inherently possess uncertainty, imprecision, and incompleteness for data representation. Two sentences may be equivalent in their meanings despite having different vector space representation while Fuzzy Rough Sets incorporates the meanings of sentences. Fuzzy Rough Set-based sentence similarity for Text Summarization has not been proposed in literature before the present work. The contribution of the research is two-fold, namely (i) Fuzzy Rough Set-based sentence similarities has been proposed and validated on SICK2014 dataset. (ii) The proposed similarities between the sentences are thereby proposed for Single document Text Summarization and evaluated for DUC2002 dataset. Experimental results confirm the applicability and efficiency of using the proposed models for both sentence similarity computations as well as for summarization.