A Survey of Automated Text Simplification

TLDR

Text simplification modifies syntax and lexicon to improve understandability and is applied to second‑language learning, preprocessing, and assistive technology, with approaches ranging from lexical to hybrid techniques, and has rapidly become a distinct research field. The survey identifies and classifies simplification research from 1998‑2013, explores current challenges, and provides an overview of contemporary work in the field. The authors conduct a literature survey from 1998‑2013, classifying studies and analyzing challenges and historical development.

Abstract

Text simplification modifies syntax and lexicon to improve the understandability of language for an end user. This survey identifies and classifies simplification research within the period 1998-2013. Simplification can be used for many applications, including: Second language learners, preprocessing in pipelines and assistive technology. There are many approaches to the simplification task, including: lexical, syntactic, statistical machine translation and hybrid techniques. This survey also explores the current challenges which this field faces. Text simplification is a non-trivial task which is rapidly growing into its own field. This survey gives an overview of contemporary research whilst taking into account the history that has brought text simplification to its current state.