An Approach for Summarizing Text Using Sentence Scoring with Key Optimizer

Abstract

There is an enormous amount of textual content rolled out over the web, which performs automatic text summarization efficiently. Specifically, extracting the multi-keywords from the textual content produces the summary from the source document by reducing the isolating text. In recent research, these summarization approaches and the problems related to this process are easily addressed with the optimization approaches. In existing research, most investigators concentrate on single-objective solutions; however, multi-objective approaches provide solutions to various issues during summarization. This work adopts a Keyword-based Elephant Yard Optimization (KEY) approach that improves the summarization process. In KEY, the analysis of the elephant movement is performed based on the group (cluster) of elephants. The significance of the movement relies on the priority given to the head. Accordingly, the textual contents are optimized based on the clustering priority. The analysis is performed over the available online datasets for text summarization to provide multiple solutions by handling multi-objective problems. Some of the predominant metrics like ROUGE-1 and ROUGE-2 score and kappa coefficient are evaluated to attain superior outcomes.