Opposition Differential Evolution Based Method for Text Summarization

The Evolutionary Algorithms (EAs) save sufficient data about problem features, search space, and population information during the runtime. Accordingly, the machine learning (ML) techniques were employed for examining these data to improve the EAs search performance compared with their classical versions. This paper employs an Opposition-Based Learning as ML approach for enhancing the initial population of the Differential Evolution algorithm in problem of text summarization. In addition, it investigates the use of the OBL technique in integer-based evolutionary populations. The objective of this proposed enhancement is to adjust the algorithm booting instead of relying on random numbers generations only. Basically, all methodology steps in this paper were presented by a previous study whereas the differences between both of them will be shown later. So, this paper tries to estimate the improvement size the OBL can achieve and compare the results with a traditional DE-based text summarization application and other baseline methods. The DUC2002 data set was assigned as a test bed and the ROUGE toolkit used to evaluate the methods performances. The experimental results showed that our proposed method assured the need for learning and improve the random-based EAs before proceed generating the solutions. The study findings conclude that our proposed method outperformed a classical DE and other baseline methods in terms of F-measure. OBL was broadly tested before in numerical test beds, in this paper it will be tested on text-based test bed news article of text summarization problem.