  Text Simplification (TS) aims to reduce the linguistic complexity of content
to make it easier to understand. Research in TS has been of keen interest,
especially as approaches to TS have shifted from manual, hand-crafted rules to
automated simplification. This survey seeks to provide a comprehensive overview
of TS, including a brief description of earlier approaches used, discussion of
various aspects of simplification (lexical, semantic and syntactic), and latest
techniques being utilized in the field. We note that the research in the field
has clearly shifted towards utilizing deep learning techniques to perform TS,
with a specific focus on developing solutions to combat the lack of data
available for simplification. We also include a discussion of datasets and
evaluations metrics commonly used, along with discussion of related fields
within Natural Language Processing (NLP), like semantic similarity.
