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            <namePart type="given">Guillermo</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>This paper demonstrates our system for shared task 4 of #SMM4H-HeaRD 2026 Workshop where a given doctor-patient dialogue is summarized into a clinical note in the corresponding SOAP format. Our proposed solution includes semi-supervised learning together with parameter efficient finetuning (PEFT) applied to a lightweight pre-trained QWEN3.5 model. Our model delivers competitive performance relative to its parameter count, and generalizes its performance to unseen test dataset.</abstract>
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%0 Conference Proceedings
%T FU-HU-P5 at #SMM4H-HeaRD 2026: MedSynth Dialogue-to-Note Generation
%A Wong, Jessica Ying En
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F wong-2026-fu
%X This paper demonstrates our system for shared task 4 of #SMM4H-HeaRD 2026 Workshop where a given doctor-patient dialogue is summarized into a clinical note in the corresponding SOAP format. Our proposed solution includes semi-supervised learning together with parameter efficient finetuning (PEFT) applied to a lightweight pre-trained QWEN3.5 model. Our model delivers competitive performance relative to its parameter count, and generalizes its performance to unseen test dataset.
%U https://aclanthology.org/2026.smm4h-1.38/
%P 237-239

Markdown (Informal)

[FU-HU-P5 at #SMM4H-HeaRD 2026: MedSynth Dialogue-to-Note Generation](https://aclanthology.org/2026.smm4h-1.38/) (Wong, SMM4H 2026)

• FU-HU-P5 at #SMM4H-HeaRD 2026: MedSynth Dialogue-to-Note Generation (Wong, SMM4H 2026)

ACL

• Jessica Ying En Wong. 2026. FU-HU-P5 at #SMM4H-HeaRD 2026: MedSynth Dialogue-to-Note Generation. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 237–239, San Diego, United States. Association for Computational Linguistics.