A Hybrid Approach for Text Summarization Using Social Mimic Optimization Algorithm

Every day, millions of Internet users share a lot of information on the web. In this digital era, the exponential growth of data on the web causes difficulties in getting the needed information quickly. Text summarization plays a crucial role in getting the needed information quickly. This work introduces a new extractive single-document summarization technique using the hybrid social mimic optimization algorithm. The objective function of the proposed work maximizes the summary sentences informative score and the sentence coherence factor. In this study, we used the three popular benchmark datasets (DUC2002, BBC News, and CNN) for the experimental work. In this study, we used ROUGE score as a performance evaluation measure and compared with five state-of-the-art single-document summarization techniques. The performance comparison analysis shows that the proposed summarization technique outperforms the other competitor approaches.