用小模型实现高质量病历摘要，解决长文本丢上下文问题。
Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension:Testing and Evaluation of the NBCE Method
- 通过动态扩展上下文，逐句引用避免信息丢失
- 70亿参数模型在ROUGE-L上接近1750亿参数的Gemini
- 适合资源有限但需高精度病历自动摘要的场景
生成患者临床记录摘要对减轻文档负担至关重要。当前人工摘要使医护人员压力巨大。我们提出一种基于大语言模型的自动方法，但长输入会导致模型丢失上下文，尤其影响小型模型表现。采用70亿参数的open-calm-7b模型，结合原生贝叶斯上下文扩展（NBCE）与重新设计的解码机制，每次仅参考一句，保持输入在2048令牌的上下文窗口内。在200个样本测试中，改进模型在ROUGE-L指标上接近谷歌超过1750亿参数的Gemini，表明以更少资源实现高性能，显著提升电子病历自动摘要的可行性。
原文摘要 · Abstract (English)
Summarizing patient clinical notes is vital for reducing documentation burdens. Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context, reducing output quality especially in small size model. We used a 7B model, open-calm-7b, enhanced with Native Bayes Context Extend and a redesigned decoding mechanism to reference one sentence at a time, keeping inputs within context windows, 2048 tokens. Our improved model achieved near parity with Google's over 175B Gemini on ROUGE-L metrics with 200 samples, indicating strong performance using less resources, enhancing automated EMR summarization feasibility.
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