ISSE: a new iterative sentence scoring and extraction scheme for automatic text summarization

Abstract

An ever-increasing volume of contextual data appearing on various types of media overwhelms users sharing information, as well as bringing up storage concerns. Automatic text summarization mitigates these challenges and helps users cope more efficiently by generating more accurate and representative summaries. In this paper, we propose a new extractive, multi-document summarization method based on a new iterative sentence scoring and extraction scheme which uses the lexical chain and clustering concepts. The evaluation is done by a well-known standard evaluator in terms of conventional measures of performance, Recall, Precision, and F-score. The results show definite improvement at reasonable compression rates of around 10%, compared to other major benchmark summarizers in the literature.

Keywords:

• Automatic text summarization
• NLP
• sentences scoring
• extractive summarization
• sentences clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).