Automatic Arabic Summarization: A survey of methodologies and systems

Lamees Mahmoud Al Qassem, Di Wang, Zaid Al Mahmoud, Hassan Barada, Ahmad Al-Rubaie, Nawaf I. Almoosa

Research output: Contribution to journalConference articlepeer-review

32 Scopus citations

Abstract

Text summarization has been a field of intensive research over the last 50 years, especially for commonly-used and relatively simple-grammar languages such as English. Moreover, the unprecedented growth in the amount of online information available in many languages to users and businesses, including news articles and social media, has made it difficult and time consuming for users to identify and consume sought after content. Hence, an automatic text summarization for various languages to generate accurate and relevant summaries from the huge amount of information available is essential nowadays. Techniques and methodologies for Arabic text summarization are still immature due to the inherent complexity of the Arabic language in terms of both structure and morphology. This paper describes the main challenges for Arabic text summarization and surveys the various methodologies and systems in the literature. This survey would be a good basis for the design of an Arabic automatic text summarization that combines the various "good" features of the existing systems and dismiss the "not-so-good" features.

Original languageBritish English
Pages (from-to)10-18
Number of pages9
JournalProcedia Computer Science
Volume117
DOIs
StatePublished - 2017
Event3rd International Conference on Arabic Computational Linguistics, ACLing 2017 - Dubai, United Arab Emirates
Duration: 5 Nov 20176 Nov 2017

Keywords

  • Arabic Natural Language Processing
  • Arabic Summarization systems
  • Automatic Text Summarization

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