Hybrid Algorithm Based on Chicken Swarm Optimization and Genetic Algorithm for Text Summarization

Abstract

The daily massive flow of information requires automated summarization methods to extract the most important information. Manual summarization of large text documents is very complicated and time-intensive for human beings. Numerous methods rate all of them decadently based on the sentence scoring that labels ratings for input sentences. The higher-rated sentences are used as a part of the summary. In an extractive-based automated text summary, locating the relevant sentences is an essential problem for the researchers. Therefore, to deal with such problems, evolutionary algorithms are applied as a solution. This paper presents a hybrid approach (CSOGA) based on the effectiveness and convergence to the solution of a (CSO) chicken swarm optimization and a (GA) genetic algorithm for text summarization to ensure the optimal solution. The evaluations of the proposed algorithms are done on the standard dataset from CNN / Daily Mail and are measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The performance of the proposed method is then compared with other methods. The results show that the new approach hybrid (CSOGA) has the best performance on text summarization quality. The proposed method was capable of generating a better accuracy than other algorithms on the ROUGE-1, ROUGE-2 and ROUGE-L. The highest increase in the accuracy of the proposed method was in ROUGE-1 with a rise of 4.4%, ROUGE-2 with a rise of 12.01%, and ROUGE-L with a rise of 9.8% comparing with the highest accuracy of the other extractive models.