@inproceedings{yuan-etal-2025-lcds,
title = "{LCDS}: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review",
author = "Yuan, Cheng and
Rui, Xinkai and
Fan, Yongqi and
Fan, Yawei and
Zhong, Boyang and
Wang, Jiacheng and
Zhang, Weiyan and
Ruan, Tong",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.28/",
doi = "10.18653/v1/2025.acl-demo.28",
pages = "284--294",
ISBN = "979-8-89176-253-4",
abstract = "Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yuan-etal-2025-lcds">
<titleInfo>
<title>LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review</title>
</titleInfo>
<name type="personal">
<namePart type="given">Cheng</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xinkai</namePart>
<namePart type="family">Rui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yongqi</namePart>
<namePart type="family">Fan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yawei</namePart>
<namePart type="family">Fan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Boyang</namePart>
<namePart type="family">Zhong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiacheng</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weiyan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Ruan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pushkar</namePart>
<namePart type="family">Mishra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Smaranda</namePart>
<namePart type="family">Muresan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tao</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-253-4</identifier>
</relatedItem>
<abstract>Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.</abstract>
<identifier type="citekey">yuan-etal-2025-lcds</identifier>
<identifier type="doi">10.18653/v1/2025.acl-demo.28</identifier>
<location>
<url>https://aclanthology.org/2025.acl-demo.28/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>284</start>
<end>294</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review
%A Yuan, Cheng
%A Rui, Xinkai
%A Fan, Yongqi
%A Fan, Yawei
%A Zhong, Boyang
%A Wang, Jiacheng
%A Zhang, Weiyan
%A Ruan, Tong
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F yuan-etal-2025-lcds
%X Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.
%R 10.18653/v1/2025.acl-demo.28
%U https://aclanthology.org/2025.acl-demo.28/
%U https://doi.org/10.18653/v1/2025.acl-demo.28
%P 284-294
Markdown (Informal)
[LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review](https://aclanthology.org/2025.acl-demo.28/) (Yuan et al., ACL 2025)
ACL
- Cheng Yuan, Xinkai Rui, Yongqi Fan, Yawei Fan, Boyang Zhong, Jiacheng Wang, Weiyan Zhang, and Tong Ruan. 2025. LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 284–294, Vienna, Austria. Association for Computational Linguistics.