PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English

2026-08-02

AI Summary

This study addresses the limitation of existing medical corpora, which typically employ a binary readability distinction between expert and lay audiences, thereby hindering fine-grained research on text simplification. To overcome this, the authors construct the first multilingual corpus spanning German and English, aligned across four levels of comprehension—reference, explanation, decision support, and accessibility—ensuring cross-lingual consistency in both topic and communicative function. The corpus integrates data from diverse sources including MSD, Gesund.Bund, Apotheken Umschau Einfache Sprache, and NHS, and is evaluated through established readability metrics alongside prompting experiments with large language models. Findings reveal that current readability measures generalize poorly across multiple comprehension levels, and that state-of-the-art open-source models often retain residual complexity during simplification, underscoring the corpus’s critical role in advancing precise, context-aware medical language simplification.

Abstract

We introduce PlainMedScale, a topic-aligned medical corpus spanning four levels of comprehensibility in German and English, drawn from MSD (professional and consumer), Gesund.Bund, Apotheken Umschau Einfache Sprache, and the NHS. The four tiers correspond to distinct communicative functions --- reference, explanation, decision support, and access --- and move beyond the binary expert--lay contrast of prior corpora. In two pilot studies enabled by the alignments, we show that many readability metrics established on two registers fail to generalize across the full gradient, and that a SOTA open-weight LLM prompted for Plain Language still partially preserves the difficulty of its input. Code (https://github.com/GS-Uni-Heidelberg/PlainMedScale) and data (https://doi.org/10.5281/zenodo.21728290) are made available.

Problem

• medical text simplification
• readability levels
• health communication
• corpus development
• plain language

Innovation

• multi-level simplification
• medical corpus
• readability metrics
• plain language generation
• cross-register alignment