Soft Rough Set based span for unsupervised keyword extraction

The present work proposes an application of Soft Rough Set and its span for unsupervised keyword extraction. In recent times Soft Rough Sets are being applied in various domains, though none of its applications are in the area of keyword extraction. On the other hand, the concept of Rough Set based span has been developed for improved efficiency in the domain of extractive text summarization. In this work we amalgamate these two techniques, called Soft Rough Set based Span (SRS), to provide an effective solution for keyword extraction from texts. The universe for Soft Rough Set is taken to be a collection of words from the input texts. SRS provides an ideal platform for identifying the set of keywords from the input text which cannot always be defined clearly and unambiguously. The proposed technique uses greedy algorithm for computing spanning sets. The experimental results suggest that extraction of keywords using the proposed scheme gives consistent results across different domains. Also, it has been found to be more efficient in comparison with several existing unsupervised techniques.