  Lexical Simplification (LS) is the task of replacing complex for simpler
words in a sentence whilst preserving the sentence's original meaning. LS is
the lexical component of Text Simplification (TS) with the aim of making texts
more accessible to various target populations. A past survey (Paetzold and
Specia, 2017) has provided a detailed overview of LS. Since this survey,
however, the AI/NLP community has been taken by storm by recent advances in
deep learning, particularly with the introduction of large language models
(LLM) and prompt learning. The high performance of these models sparked renewed
interest in LS. To reflect these recent advances, we present a comprehensive
survey of papers published between 2017 and 2023 on LS and its sub-tasks with a
special focus on deep learning. We also present benchmark datasets for the
future development of LS systems.
