用思维树启发的混合方法，提升法律判決摘要质量。
A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs
- 借鉴思维树思路，融合抽取与摘要生成
- 在DeepSeek和LLama上测试，混合方法效果最优
- 适合需要精准法律摘要的研究者与从业者
近年来，大语言模型（LLMs）被越来越多地用于法律判決摘要任务。以往研究多集中于传统的抽取式或抽象式摘要，而混合式（抽取-抽象）方法尚未得到充分探索。本文提出一种受思维树启发的抽取-抽象混合摘要方法，用于法律判決摘要。我们使用DeepSeek和LLama两款主流LLM进行实验，对比了抽取式、抽象式及混合式摘要的效果。实验结果表明，所提出的抽取-抽象提示策略生成的摘要质量优于其他提示方式。
原文摘要 · Abstract (English)
In recent times, Large Language Models (LLMs) are increasingly being used for legal case judgement summarization. Most prior works have tried traditional extractive and abstractive summarization of case judgements. However, hybrid or extractive-abstractive techniques have not been explored much. In this work, we propose a novel tree-of-thoughts inspired extractive-abstractive summarization approach for legal judgement summarization. We conduct experiments using two popular LLMs, DeepSeek and LLama, and compare among extractive, abstractive and extractive-abstractive summarization. Our experiments show that the proposed extractive-abstractive prompt provides better summaries compared to other types of LLM prompts.
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