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        <title>Annotation Guidelines and Challenges for Automatic Simplification of Portuguese Drug Leaflets</title>
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    <abstract>While most essential medicines have become widely accessible across all social strata in Brazil due to government initiatives and market shifts, a significant barrier remains: the technical complexity of medication leaflets. This pragmatic and linguistic gap hinders patient comprehension of critical risks and benefits. Thus, adapting these texts into plain language patterns is crucial for patient safety and treatment adherence. Large language models have been increasingly effective as practical solutions for text simplification, an important Natural Language Processing (NLP) task that serves as a basis for several other linguistic and computational tasks. However, the scarcity of annotated datasets remains a bottleneck for rigorous evaluation. To bridge this gap, we propose a streamlined pipeline for generating simplified medical leaflets and introduce an initial benchmark dataset of 30 expertly annotated samples. Our results, supported by semantic and morphosyntactic evaluations, demonstrate that the proposed method produces high-quality, simplified content suitable for health applications.</abstract>
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%0 Conference Proceedings
%T Annotation Guidelines and Challenges for Automatic Simplification of Portuguese Drug Leaflets
%A Scalercio, Arthur
%A Bertotto, Eduarda
%A Jesus, Silvana
%A Finatto, Maria José
%A Paes, Aline
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F scalercio-etal-2026-annotation
%X While most essential medicines have become widely accessible across all social strata in Brazil due to government initiatives and market shifts, a significant barrier remains: the technical complexity of medication leaflets. This pragmatic and linguistic gap hinders patient comprehension of critical risks and benefits. Thus, adapting these texts into plain language patterns is crucial for patient safety and treatment adherence. Large language models have been increasingly effective as practical solutions for text simplification, an important Natural Language Processing (NLP) task that serves as a basis for several other linguistic and computational tasks. However, the scarcity of annotated datasets remains a bottleneck for rigorous evaluation. To bridge this gap, we propose a streamlined pipeline for generating simplified medical leaflets and introduce an initial benchmark dataset of 30 expertly annotated samples. Our results, supported by semantic and morphosyntactic evaluations, demonstrate that the proposed method produces high-quality, simplified content suitable for health applications.
%U https://aclanthology.org/2026.propor-2.19/
%P 121-127

Markdown (Informal)

[Annotation Guidelines and Challenges for Automatic Simplification of Portuguese Drug Leaflets](https://aclanthology.org/2026.propor-2.19/) (Scalercio et al., PROPOR 2026)

• Annotation Guidelines and Challenges for Automatic Simplification of Portuguese Drug Leaflets (Scalercio et al., PROPOR 2026)

ACL

• Arthur Scalercio, Eduarda Bertotto, Silvana Jesus, Maria José Finatto, and Aline Paes. 2026. Annotation Guidelines and Challenges for Automatic Simplification of Portuguese Drug Leaflets. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2, pages 121–127, Salvador, Brazil. Association for Computational Linguistics.