Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods. In this study, attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision, continuity and consistency. To investigate the efficiency of the proposed algorithm, the results of the study were evaluated with respect to the precision and recall criteria. The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.