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

Abstract

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.

KEYWORDS:

• Fuzzy Rough Sets
• Fuzzy sets
• Rough Sets
• Text Summarization
• Sentence similarity
• Upper approximation
• Lower approximation

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Nidhika Yadav http://orcid.org/0000-0001-5387-2740

Additional information

Notes on contributors

Niladri Chatterjee

Niladri Chatterjee is a Professor of Statistics and Computer Science in the Department of Mathematics, IIT Delhi. His primary research areas are Natural Language Processing, Semantic Web, Statistical Modeling. He obtained PhD in Computer Science from University College London. He has more than 90 publications in international and national journals and conferences. He has been the Organizing Chair of “CICLING – 2012” in March 2012. He is the recipient of Commonwealth Scholarship for Post Graduate Research. He has also been a Visiting Professor in Dipartimento di Informatica, University of Pisa, Italy. He has supervised 8 PhDs and more than 80 Masters’ thesis so far. Currently, 5 PhD students are working under his supervision.

Nidhika Yadav

Nidhika Yadav is a PhD scholar in Department of Mathematics, IIT Delhi. She has experience working in academics and software development. Her current research interests include Soft Computing, AI and NLP. Email: [email protected]