An improved evolutionary algorithm for extractive text summarization

Abstract

The main challenge of extractive-base text summarization is in selecting the top representative sentences from the input document. Several techniques were proposed to enhance the process of selection such as feature-base, cluster-base, and graph-base methods. Basically, this paper proposed to enhance a previous work, and provides some limitations in the similarity calculation of that previous work. This paper proposes an enhanced mixed feature-base and cluster-base approaches to produce a high qualified single-document summary. We used the Jaccard similarity measure to adjust the sentence clustering process instead of using the Normalized Google Distance (NGD) similarity measure. In addition, this paper proposes a new real-to-integer values modulator instead of using the genetic mutation operator which was adopted in the previous work. The Differential Evolution (DE) algorithm is used for train and test the proposed methods. The DUC2002 dataset was preprocessed and used as a test bed. The results show that our proposed differential mutant presented a satisfied performance while the Genetic mutant proved to be the better. In addition, our analysis of NGD similarity scores showed that NGD was an inappropriate selection in the previous study as it performs successfully in a very big database such as Google. Our selection of Jaccard measure was fortunate and obtained superior results surpassed the NGD using the new proposed modulator and the genetic operator. In addition, both algorithms outperformed the standard baseline Microsoft Word Summarizer and Copernic methods.