# A Survey on Text Simplification

arXiv: https://arxiv.org/abs/2008.08612 · HTML: https://mlsanity.com/paper/2008.08612/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [A Review of Human Evaluation for Style Transfer](https://mlsanity.com/paper/2106.04747/) (2021)
- [How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks](https://mlsanity.com/paper/2109.04604/) (2021)
- [Summarization, Simplification, and Generation: The Case of Patents](https://mlsanity.com/paper/2104.14860/) (2021)
- [Towards Arabic Sentence Simplification via Classification and Generative Approaches](https://mlsanity.com/paper/2204.09292/) (2022)
- [Word-order typology in Multilingual BERT: A case study in subordinate-clause detection](https://mlsanity.com/paper/2205.11987/) (2022)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [SimpLex: a lexical text simplification architecture](https://mlsanity.com/paper/2304.07002/) (2023)
- [Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models](https://mlsanity.com/paper/2309.12551/) (2023)
- [Machine Translation Testing via Syntactic Tree Pruning](https://mlsanity.com/paper/2401.00751/) (2024)
- [SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words](https://mlsanity.com/paper/2204.07779/) (2022)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)

## Abstract (arXiv)

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.