@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}