CSTS: Cuckoo search based model for text summarization

Abstract

Exponential growth of information in the web became infeasible for user to sieve useful information very quickly. So solution to such problem now a day is text summarization. Text summarization is the process of creating condensed version of original text by preserving important information in it. This paper presents for the first time a nature inspired cuckoo search optimization algorithm for optimal selection of sentences as summary sentence of intelligent text summarizer. The key aspects of proposed summarizer focus on content coverage and length while reducing redundant information in the summaries. To solve this optimization problem, this model uses inter-sentence relationship and sentence-to-document relationship by considering widely used similarity measure cosine similarity. The inputs for this model are taken from DUC dataset. Whereas the result is evaluated by ROUGE tool and compared with state-of-the-art approaches, in which our model in multi-document summarization have shown significant result than others.