Approaches, Methods, and Resources for Assessing the Readability of Arabic Texts

Abstract

Text readability assessment is a well-known problem that has acquired even more importance in today’s information-rich world. In this article, we survey various approaches to measuring and assessing the readability of texts. Our specific goal is to provide a perspective on the state-of-the-art in readability assessment research for Arabic, which differs significantly from other languages on which readability studies have tended to focus. We provide background on readability assessment research and tools for English, for which readability studies are the most advanced. We then survey approaches adopted for Arabic, both classical formula-based approaches and studies that combine Machine Learning (ML) with Natural Language Processing (NLP) techniques. The works we cover target text corpora for different audiences: school-age first language readers (L1), foreign language learners (L2), and adult readers in non-academic contexts. Therefore, we explore differences between reading in L1 and L2 and consider how they play out specifically in Arabic after describing language characteristics that may impact readability. Finally, we highlight challenges for Arabic readability research and propose multiple future directions to improve readability assessment and related applications that would benefit from more attention.