Recent Advances in Arabic Automatic Text Summarization

Abstract

Recently, the volume of the Arabic texts and documents on the internet had increased rabidly and generated a rich and valuable content on the www. Several parties had contributed to this content, this includes researchers, companies, governmental agencies, educational institutions, etc. With this big content it became difficult to search and extract useful information using only mankind skills and search engines. This motivated researchers to propose automated methodologies to extract summaries or useful information from those documents. A lot of research has been proposed for the automatic extraction of summaries for the English language and other languages. Unfortunately, the research for the Arabic automatic text summarization is still humble and needs more attention. This study presents a critical review and analysis of recent studies in Arabic automatic text summarization. The review includes all recent studies used the different text summarization approaches which include statistical-based, graph-based, evolutionary-based, and machine learning-based approaches. The selection criteria of the literature are based on the venue of publication and year of publication; back to five years. All review papers in Arabic ATS are excluded from the review since the study considers the recent methodologies in Arabic ATS. As a conclusion of this research, we recommend researchers in Arabic text summarization to investigate the use of machine learning on abstractive approach for text summarization due to the lack of research in this area. Keywords: Automatic Text Summarization, The Arabic Language, Machine Learning, Natural Language Processing, Text Processing, Computational Linguistics.