# Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts

arXiv: https://arxiv.org/abs/2302.09155 · HTML: https://mlsanity.com/paper/2302.09155/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 64 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 30 of 32)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://mlsanity.com/paper/2005.00481/) (2020)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [SciFive: a text-to-text transformer model for biomedical literature](https://mlsanity.com/paper/2106.03598/) (2021)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Felix: Flexible Text Editing Through Tagging and Insertion](https://mlsanity.com/paper/2003.10687/) (2020)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [Paragraph-level Simplification of Medical Texts](https://mlsanity.com/paper/2104.05767/) (2021)
- [Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen](https://mlsanity.com/paper/2005.00701/) (2020)
- [General-Purpose Question-Answering with Macaw](https://mlsanity.com/paper/2109.02593/) (2021)
- [Question-Driven Summarization of Answers to Consumer Health Questions](https://mlsanity.com/paper/2005.09067/) (2020)
- [EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start](https://mlsanity.com/paper/2205.12209/) (2022)
- [StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer](https://mlsanity.com/paper/2104.05196/) (2021)
- [Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification](https://mlsanity.com/paper/2010.10035/) (2020)
- [Text Simplification by Tagging](https://mlsanity.com/paper/2103.05070/) (2021)
- [Multiple-Attribute Text Style Transfer](https://mlsanity.com/paper/1811.00552/) (2018)
- [AutoMeTS: The Autocomplete for Medical Text Simplification](https://mlsanity.com/paper/2010.10573/) (2020)

## Built on by (most cited first, 16 of 16)

- [BLESS: Benchmarking Large Language Models on Sentence Simplification](https://mlsanity.com/paper/2310.15773/) (2023)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Multilingual Simplification of Medical Texts](https://mlsanity.com/paper/2305.12532/) (2023)
- [Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation](https://mlsanity.com/paper/2508.08730/) (2025)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness](https://mlsanity.com/paper/2311.11211/) (2023)
- [On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization](https://mlsanity.com/paper/2403.05788/) (2024)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [PERCS: Persona-Guided Controllable Biomedical Summarization Dataset](https://mlsanity.com/paper/2512.03340/) (2025)
- [Simplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs](https://mlsanity.com/paper/2601.05794/) (2026)
- [JEBS: A Fine-grained Biomedical Lexical Simplification Task](https://mlsanity.com/paper/2506.12898/) (2025)
- [Text Simplification with Sentence Embeddings](https://mlsanity.com/paper/2510.24365/) (2025)
- [OasisSimp: An Open-source Asian-English Sentence Simplification Dataset](https://mlsanity.com/paper/2603.14111/) (2026)
- [Taming CATS: Controllable Automatic Text Simplification through Instruction Fine-Tuning with Control Tokens](https://mlsanity.com/paper/2604.01779/) (2026)
- [PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English](https://mlsanity.com/paper/2608.01158/) (2026)

## Abstract (arXiv)

Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (underlineMedical dataset for underlineElaborative and underlineAbstractive underlineSimplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its textitexpert-layman-AI collaborative annotations facilitate textitcontrollability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of textitcontrollability into text simplification, by using a multi-angle training approach: textitposition-aware, which uses in-place annotated inputs and outputs, and textitposition-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, textitposition-aware control generates better simplification than the textitposition-agnostic one. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.