Enhancing metaheuristic based extractive text summarization with fuzzy logic.

In today's world as the data on the web is increasing it becomes a challenge to identify the relevant information. Automatic text summarization (ATS) provides a significant answer to it. In this paper, fuzzy logic and shark smell optimization (SSO) based algorithm for extractive text summarization is proposed. Shark Smell Optimization has been used to assign a weight to eight different features to identify the less and more important text features of text summarization. Then, the Fuzzy Logic's inference system is utilized to generate fuzzy rules, and finally an automated summary is generated. The system generated summaries have been tested against the reference summaries from the DUC 2002, DUC 2003, DUC-2004 and TAC-11 dataset and ROUGE toolkit has been used for the evaluation of the proposed solution. Results of the proposed algorithm are compared against traditional methods and the rouge score suggested that the proposed algorithm generates better results than other methods. [ABSTRACT FROM AUTHOR]