领域知识注入在法律文件摘要中的优势：以总结印度法院判决的英文和印地语摘要为例
Advantages of Domain Knowledge Injection for Legal Document Summarization: A Case Study on Summarizing Indian Court Judgments in English and Hindi
AI总结：
本研究通过注入领域知识提升印度法律文件摘要能力，生成英文和印地语摘要，验证方法有效性。
摘要：
总结印度法律法院判决是一项复杂任务，不仅因为法律文本语言复杂且结构不规范，还因为印度大部分人口不理解法律文本中使用的复杂英语，因此需要生成印地语等印度语言的摘要。在本研究中，我们旨在通过将领域知识注入多种摘要模型中，提高印度法律文本的摘要能力，以生成英文和印地语（印度最广泛使用的语言）的摘要。我们提出了一种框架，通过将专门针对法律文本预训练的编码器融入提取式神经摘要模型中，以增强其性能。此外，我们还探索了通过在大规模法律语料库上进行连续预训练，将法律领域知识注入生成模型（包括大语言模型）中。我们的方法在英文到英文和英文到印地语的印度法律文件摘要中实现了统计上显著的改进，通过标准评估指标、事实一致性指标和法律领域特定指标进行测量。此外，这些改进通过领域专家验证，证明了我们方法的有效性。
英文摘要：
Summarizing Indian legal court judgments is a complex task not only due to the intricate language and unstructured nature of the legal texts, but also since a large section of the Indian population does not understand the complex English in which legal text is written, thus requiring summaries in Indian languages. In this study, we aim to improve the summarization of Indian legal text to generate summaries in both English and Hindi (the most widely spoken Indian language), by injecting domain knowledge into diverse summarization models. We propose a framework to enhance extractive neural summarization models by incorporating domain-specific pre-trained encoders tailored for legal texts. Further, we explore the injection of legal domain knowledge into generative models (including Large Language Models) through continual pre-training on large legal corpora in English and Hindi. Our proposed approaches achieve statistically significant improvements in both English-to-English and English-to-Hindi Indian legal document summarization, as measured by standard evaluation metrics, factual consistency metrics, and legal domain-specific metrics. Furthermore, these improvements are validated through domain experts, demonstrating the effectiveness of our approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
