Supervised Weight Learning-Based PSO Framework for Single Document Extractive Summarization

The need for automatic text summarization is natural: there is a huge volume of information available online, which prompts for a widespread interest in extracting relevant information in a concise and understandable manner. Here, automated text summarization has been treated as an extractive single -document summarization problem in the proposed system. To solve this problem, a particle swarm optimisation (PSO) algorithmbased approach is suggested, with the goal of producing good summaries in terms of content coverage, informativeness, and readability. This paper introduces XSumm-PSO: a new approach based on PSO optimization technique in a supervised manner for extractive summarization. Further, this paper also contributes a new feature "incorrect word " that captures misspelled words in the candidate sentences. This feature is combined with nine existing features used by proposed model to generate error free summaries. As a result, the proposed XSumm-PSO framework produces superior performance achieving improvements of +2.7%, +0.8%, and +0.8% for ROUGE -1, ROUGE -2, and ROUGE -L scores, respectively, on DUC 2002 dataset, over state-of-the-art techniques. The corresponding improvements on the CNN/DailyMail dataset are +0.97%, +0.25%, and +0.49%. We also performed sample t -test, showing the proposed approach is statistically consistent across various runs.