Towards Text Simplification in Spanish: A Brief Overview of Deep Learning Approaches for Text Simplification

Text simplification refers to the transformation of a specific source text into a target text aiming to increase understanding and readability for one or more specific audiences. This task demands large human efforts and specialized knowledge, which makes the usage of automated or semi-automated computational approaches appealing. The rise of deep learning as an unifying paradigm between seemingly different fields as image analysis, sound processing and natural language processing has considerably influenced the current state of the art approaches for automatic text simplification. Therefore, in this work, we focus on the study of deep learning based state of the art methods for automatic text simplification in the Spanish language. For this end, we first disentangle the different tasks which can be addressed in order to yield a simplified text in general. Later we review the latest deep learning-based approaches, along with the main datasets and performance metrics used in the field. We also describe approaches to deal with small datasets and technical words. Finally, we describe some lessons to build accurate automatic text simplification systems in Spanish, as in this language there is a noticeable shortage of work for text simplification.