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LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review

arXiv 2507.05319 ↗First posted 7 Jul 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccurate content or fabricating information without valid sources. In addition, electronic medical records (EMRs) typically consist of long-form data, making it challenging for LLMs to attribute the generated content to the sources. To address these challenges, we propose LCDS, a Logic-Controlled Discharge Summary generation system. LCDS constructs a source mapping table by calculating textual similarity between EMRs and discharge summaries to constrain the scope of summarized content. Moreover, LCDS incorporates a comprehensive set of logical rules, enabling it to generate more reliable silver discharge summaries tailored to different clinical fields. Furthermore, LCDS supports source attribution for generated content, allowing experts to efficiently review, provide feedback, and rectify errors. The resulting golden discharge summaries are subsequently recorded for incremental fine-tuning of LLMs. Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 7 Jul 2025.

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References · 20

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  2. 2024A Survey on LLM-as-a-Judgecited by 1,423
  3. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  4. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  5. 2024ContextCite: Attributing Model Generation to Contextcited by 98
  6. 2019Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reportscited by 96
  7. 2023AlpaCare:Instruction-tuned Large Language Models for Medical Applicationcited by 90
  8. 2020Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniquescited by 64
  9. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  10. 2023Effective Large Language Model Adaptation for Improved Grounding and Citation Generationcited by 45
  11. 2024Learning to Plan and Generate Text with Citationscited by 28
  12. 2024Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"cited by 20
8 more
  1. 2021Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notescited by 19
  2. 2024Verifiable Generation with Subsentence-Level Fine-Grained Citationscited by 15
  3. 2023Towards Verifiable Text Generation with Symbolic Referencescited by 13
  4. 2024Verifiable by Design: Aligning Language Models to Quote from Pre-Training Datacited by 13
  5. 2023FaMeSumm: Investigating and Improving Faithfulness of Medical Summarizationcited by 9
  6. 2023A Method to Automate the Discharge Summary Hospital Course for Neurology Patientscited by 6
  7. 2024SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrievalcited by 5
  8. 2024UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Modelscited by 4

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