Fuzzy Inference-Propelled Sentence Ranking for Extractive Summary Generation

Natural language serves as an impeccable tool for the appropriate representation of knowledge among individuals. Owing to the varying representation of the same knowledge base and the perpetual growth of the world wide web, the need to uncover an effective method to condense available textual data without significantly dampening the implied information is paramount. In an attempt to solve the need for effectively condensing textual data, the paper proposes a system which is capable of mimicking the human brain's approach to process natural language fuzzy logic. The system is subjected to both intrinsic and extrinsic evaluation, and the results are compared against two other text summarizers—Auto Summarize Tool and SweSum—using the CNN Corpus Dataset. The relevance prediction measure, F1 score, and recall results suggest the applicability of fuzzy reasoning in text summarization, and through evaluation, it can be inferred that proposed system has successfully tried to mimic the process of summary generation by the human brain.