An efficient single document Arabic text summarization using a combination of statistical and semantic features

Abstract

The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel for-mulation is used taking into account sentence importance, coverage and diversity. Further, two summa-rizing techniques including score-based and supervised machine learning were employed to produce thesummary and then assist leveraging the designed features. We demonstrate the effectiveness of the pro-posed method through a set of experiments under EASC corpus using ROUGE measure. Compared tosome existing related work, the experimental evaluation shows the strength of the proposed methodin terms of precision, recall, and F-score performance metrics.