Optimized Text Summarization method based on Fuzzy Logic

Abstract

Text summarization is the task of condensing a text segment into a shorter version, reducing the size of the original text context while also preserving the informational elements and the meaning of the content. Manual text summarization will involve a significant amount of time and thus become a time expensive and generally laborious task. Aiming to reduce these pitfalls in manual text summarization, automatic text summarization has been evolving now bearing a strong motivation for academic research. Text Summarization is carried out by two main approaches, namely Extraction and Abstraction. This paper utilizes the extraction process for sentence selection. We also used some feature-based sentence scoring techniques, which play an important role in text summarization. Recently fuzzy logic-based research projects have been popularized among researchers and have been extensively applied in the domain of Natural Language Processing. Our main goal in this paper is to apply fuzzy logic in the task of text summarization. Finally, we analyzed the performance metrics resulting from the fuzzy logic-based text summarization with the benchmark methods; Rule Base and Neural Network techniques for computing the values for Precision, Recall, and F-Measure. In the process of applying the Fuzzy logic, rules were used to balance the weights between important and unimportant features based on the Feature Extraction. With the experimental results achieved, it was concluded that approaching Fuzzy Logic in the process of text summarization yields more successful results than the Rule Base and Neural Network methods.