Enhancing Text Readability Using Deep Learning Techniques

Abstract

In the information era, reading becomes more important to keep up with the growing amount of knowledge. The ability to read a document varies from person to person depending on their skills and knowledge. It also depends on the readability level of the text, whether it matches the reader’s level or not. In this thesis, we propose a system that uses state-of-the-art technology in machine learning and deep learning to classify and simplify a text taking into consideration the reader’s level of reading. The system classifies any text to its equivalent readability level. If the text readability level is higher than the reader’s level, i.e. too difficult to read, the system performs text simplification to meet the desired readability level. The classification and simplification models are trained on data annotated with readability levels from in the Newsela corpus. The trained simplification model performs at sentence level, to simplify a given text to match a specific readability level. Moreover, the trained classification model is used to classify more unlabelled sentences using Wikipedia Corpus and Mechanical Turk Corpus in order to enrich the text simplification dataset. The augmented dataset is then used to improve the quality of the simplified sentences. The system generates simplified versions of a text based on the desired readability levels. This can help people with low literacy to read and understand any documents they need. It can also be beneficial to educators who assist readers with different reading levels.