@inproceedings{kalavantavanich-etal-2026-ascribe,
title = "{ASCRIBE}: Atomic and Significance-Based Reasoning for {T}hai Clinical {SOAP} Note Generation",
author = "Kalavantavanich, Tarm and
Ponarchar, Teerawut and
Arsomngern, Pattaramanee and
Wonglertsakul, Jenta and
Chuthong, Watcharakorn and
Boonnag, Chiraphat and
Pipatsrisawat, Knot and
Achakulvisut, Titipat",
editor = "Zhang, Chen and
Allaway, Emily and
M{'}hamdi, Meryem and
Limkonchotiwat, Peerat and
Bai, Richard He and
T.Y.S.S., Santosh and
Thapa, Surendrabikram and
Li, Yinqiao",
booktitle = "Proceedings of the 10th Widening {NLP} Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.winlp-main.14/",
pages = "141--172",
ISBN = "979-8-89176-472-9",
abstract = "Automatic SOAP note generation can ease the documentation burden on physicians, but existing reasoning methods often omit clinically important information and generate unsupported content. Progress in Thai is also further hindered by the lack of publicly available datasets. We propose ASCRIBE, a physician-inspired framework that ascribes a clinical-significance level to each extracted atomic fact in the conversation before summarization, making a general-purpose LLM a more reliable scribe. We also release ThaiClinicBench, the first de-identified Thai clinical summarization benchmark of real encounters, together with a synthetic training corpus. As a prompt, ASCRIBE outperforms chain-of-thought prompting on GPT-5.4 and Gemini 3.1 Pro across the physician-aligned LLM-judge metrics and improves on standard prompting by up to 10.3 points of completeness LLM-judge metric. As a GRPO reward, it enables a Gemma-4-E4B model trained solely on synthetic data to match Gemini 3.1 Pro in factual precision and surpass it in completeness. Code and data will be released upon acceptance."
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<abstract>Automatic SOAP note generation can ease the documentation burden on physicians, but existing reasoning methods often omit clinically important information and generate unsupported content. Progress in Thai is also further hindered by the lack of publicly available datasets. We propose ASCRIBE, a physician-inspired framework that ascribes a clinical-significance level to each extracted atomic fact in the conversation before summarization, making a general-purpose LLM a more reliable scribe. We also release ThaiClinicBench, the first de-identified Thai clinical summarization benchmark of real encounters, together with a synthetic training corpus. As a prompt, ASCRIBE outperforms chain-of-thought prompting on GPT-5.4 and Gemini 3.1 Pro across the physician-aligned LLM-judge metrics and improves on standard prompting by up to 10.3 points of completeness LLM-judge metric. As a GRPO reward, it enables a Gemma-4-E4B model trained solely on synthetic data to match Gemini 3.1 Pro in factual precision and surpass it in completeness. Code and data will be released upon acceptance.</abstract>
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%0 Conference Proceedings
%T ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation
%A Kalavantavanich, Tarm
%A Ponarchar, Teerawut
%A Arsomngern, Pattaramanee
%A Wonglertsakul, Jenta
%A Chuthong, Watcharakorn
%A Boonnag, Chiraphat
%A Pipatsrisawat, Knot
%A Achakulvisut, Titipat
%Y Zhang, Chen
%Y Allaway, Emily
%Y M’hamdi, Meryem
%Y Limkonchotiwat, Peerat
%Y Bai, Richard He
%Y T.Y.S.S., Santosh
%Y Thapa, Surendrabikram
%Y Li, Yinqiao
%S Proceedings of the 10th Widening NLP Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-472-9
%F kalavantavanich-etal-2026-ascribe
%X Automatic SOAP note generation can ease the documentation burden on physicians, but existing reasoning methods often omit clinically important information and generate unsupported content. Progress in Thai is also further hindered by the lack of publicly available datasets. We propose ASCRIBE, a physician-inspired framework that ascribes a clinical-significance level to each extracted atomic fact in the conversation before summarization, making a general-purpose LLM a more reliable scribe. We also release ThaiClinicBench, the first de-identified Thai clinical summarization benchmark of real encounters, together with a synthetic training corpus. As a prompt, ASCRIBE outperforms chain-of-thought prompting on GPT-5.4 and Gemini 3.1 Pro across the physician-aligned LLM-judge metrics and improves on standard prompting by up to 10.3 points of completeness LLM-judge metric. As a GRPO reward, it enables a Gemma-4-E4B model trained solely on synthetic data to match Gemini 3.1 Pro in factual precision and surpass it in completeness. Code and data will be released upon acceptance.
%U https://aclanthology.org/2026.winlp-main.14/
%P 141-172
Markdown (Informal)
[ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation](https://aclanthology.org/2026.winlp-main.14/) (Kalavantavanich et al., WiNLP 2026)
ACL
- Tarm Kalavantavanich, Teerawut Ponarchar, Pattaramanee Arsomngern, Jenta Wonglertsakul, Watcharakorn Chuthong, Chiraphat Boonnag, Knot Pipatsrisawat, and Titipat Achakulvisut. 2026. ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation. In Proceedings of the 10th Widening NLP Workshop, pages 141–172, Budapest, Hungary. Association for Computational Linguistics.