Evaluating the readability of a text can significantly facilitate the precise
expression of information in a written form. The formulation of text
readability assessment demands the identification of meaningful properties of
the text and correct conversion of features to the right readability level.
Sophisticated features and models are being used to evaluate the
comprehensibility of texts accurately. Still, these models are challenging to
implement, heavily language-dependent, and do not perform well on short texts.
Deep reinforcement learning models are demonstrated to be helpful in further
improvement of state-of-the-art text readability assessment models. The main
contributions of the proposed approach are the automation of feature
extraction, loosening the tight language dependency of text readability
assessment task, and efficient use of text by finding the minimum portion of a
text required to assess its readability. The experiments on Weebit, Cambridge
Exams, and Persian readability datasets display the model's state-of-the-art
precision, efficiency, and the capability to be applied to other languages.Comment: 8 pages, 2 figures, 6 equations, 7 table