# Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation

arXiv: https://arxiv.org/abs/2012.02420 · HTML: https://mlsanity.com/paper/2012.02420/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 5 of 5)

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- [VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models](https://mlsanity.com/paper/2411.04825/) (2024)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models](https://mlsanity.com/paper/2604.01965/) (2026)
- [Text Simplification with Sentence Embeddings](https://mlsanity.com/paper/2510.24365/) (2025)

## Abstract (arXiv)

Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are proposed to automatically translate expert language into layperson-understandable language, only a few of them focus on both accuracy and readability aspects simultaneously in the clinical domain. Thus, simplification of the clinical language is still a challenging task, but unfortunately, it is not yet fully addressed in previous work. To benchmark this task, we construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. Besides, we propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance compared with eight strong baselines. To fairly evaluate the performance, we also propose three specific evaluation metrics. Experimental results demonstrate the utility of the annotated MedLane dataset and the effectiveness of the proposed model DECLARE.