Enhanced Genetic Algorithm for Single Document Extractive Summarization

In extractive summarization, summaries are generated by selecting the most salient sentences from the original text. The text summarization can be seen as a classification of sentences into two groups: in-summary/not-in-summary. Many approaches have been proposed to extract key sentences in which using Genetic Algorithms (GAs) has shown some promising results. In this paper, we propose an enhanced genetic algorithm in order to improve the quality of extractive text summarization. More concisely, we first evaluate the role of some sentence features and their contribution to improve the fitness function. We second investigate some crossover and mutation mechanisms in order to augment the accuracy of summarization as well as the performance of our model. The experiment has been conducted for the Daily Mail dataset to assess the proposed model and previous works. The empirical results show that our proposed GA gives better accuracy in comparison with TextRank and SummaRunNer, i.e., increasing the accuracy by 7.2% and 6.9% respectively.