用法律实体信息增强模型，自动生成德语判决摘要
Summarisation of German Judgments in conjunction with a Class-based Evaluation
- 用法律实体信息增强判决文本后微调大语言模型
- 引入分类评估体系，量化摘要的语言质量与准确性
- 虽提升相关性但生成摘要仍不满足实际使用需求
自动总结长篇法律文件可为法律从业者提供有力支持。我们通过微调解码器型大语言模型，自动生成德语判决的摘要（即指导原则）。在训练前，我们为判决文本注入法律实体信息以增强上下文理解。为评估生成摘要的质量，我们定义了一套评估类别，可衡量其语言表达、相关性、完整性与正确性。实验结果表明，引入法律实体有助于模型识别关键内容，但生成摘要的整体质量尚未达到实际应用标准。
原文摘要 · Abstract (English)
The automated summarisation of long legal documents can be a great aid for legal experts in their daily work. We automatically create summaries (guiding principles) of German judgments by fine-tuning a decoder-based large language model. We enrich the judgments with information about legal entities before the training. For the evaluation of the created summaries, we define a set of evaluation classes which allows us to measure their language, pertinence, completeness and correctness. Our results show that employing legal entities helps the generative model to find the relevant content, but the quality of the created summaries is not yet sufficient for a use in practice.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。