利用综合语义和结构信息加强法律判决摘要分析

IF 3.1 2区 社会学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jingpei Dan, Weixuan Hu, Yuming Wang
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引用次数: 0

摘要

法律判决摘要(LJS)可以高度概括法律裁判文书,提高在案件检索等场合的司法工作效率。法律判决书通常很长;然而,大多数现有的LJS方法都是直接基于一般的文本摘要模型,不能有效地处理长文本。此外,由于法律裁判文书复杂的结构特点,仅采用一种摘要模型可能会丢失一些信息。为了解决这些问题,我们提出了一种综合的摘要方法,利用语义和结构信息来提高LJS的质量。具体而言,法律裁判文书首先根据其具体结构分为三个相对较短的部分。采用Lawformer作为编码器,提出了抽取型摘要模型BSLT和抽象型摘要模型LPGN。Lawformer是一种新的针对长法律文件的预训练语言模型,专门用于捕获长距离依赖关系并对法律语义特征进行建模。然后,针对其结构特点,采用不同的模型对相应的部分进行总结。最后,将得到的摘要进行整合,生成包含语义和结构信息的高质量摘要。我们进行了对比实验来评估我们模型的性能。结果表明,我们的模型在平均ROUGE得分上优于基准模型LEAD-3 14.78%,这表明我们的方法在法律司法中是有效的,并且有望应用于辅助法律人工智能的其他任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing legal judgment summarization with integrated semantic and structural information

Legal Judgment Summarization (LJS) can highly summarize legal judgment documents, improving judicial work efficiency in case retrieval and other occasions. Legal judgment documents are usually lengthy; however, most existing LJS methods are directly based on general text summarization models, which cannot handle long texts effectively. Additionally, due to the complex structural characteristics of legal judgment documents, some information may be lost by applying only one single kind of summarization model. To address these issues, we propose an integrated summarization method which leverages both semantic and structural information to improve the quality of LJS. Specifically, legal judgment documents are firstly segmented into three relatively short parts according to their specific structure. We propose an extractive summarization model named BSLT and an abstractive summarization model named LPGN by adopting Lawformer as the encoder. Lawformer is a new pre-trained language model for long legal documents, which specializes in capturing long-distance dependency and modeling legal semantic features. Then, we adopt different models to summarize the corresponding part regarding its structural characteristics. Finally, the obtained summaries are integrated to generate a high-quality summary involving semantic and structural information. We conduct comparative experiments to evaluate the performance of our model. The results show that our model outperforms the baseline model LEAD-3 by 14.78% on the mean ROUGE score, which demonstrates our method is effective in LJS and is prospected to be applied to assist other tasks in legal artificial intelligence.

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来源期刊
CiteScore
9.50
自引率
26.80%
发文量
33
期刊介绍: Artificial Intelligence and Law is an international forum for the dissemination of original interdisciplinary research in the following areas: Theoretical or empirical studies in artificial intelligence (AI), cognitive psychology, jurisprudence, linguistics, or philosophy which address the development of formal or computational models of legal knowledge, reasoning, and decision making. In-depth studies of innovative artificial intelligence systems that are being used in the legal domain. Studies which address the legal, ethical and social implications of the field of Artificial Intelligence and Law. Topics of interest include, but are not limited to, the following: Computational models of legal reasoning and decision making; judgmental reasoning, adversarial reasoning, case-based reasoning, deontic reasoning, and normative reasoning. Formal representation of legal knowledge: deontic notions, normative modalities, rights, factors, values, rules. Jurisprudential theories of legal reasoning. Specialized logics for law. Psychological and linguistic studies concerning legal reasoning. Legal expert systems; statutory systems, legal practice systems, predictive systems, and normative systems. AI and law support for legislative drafting, judicial decision-making, and public administration. Intelligent processing of legal documents; conceptual retrieval of cases and statutes, automatic text understanding, intelligent document assembly systems, hypertext, and semantic markup of legal documents. Intelligent processing of legal information on the World Wide Web, legal ontologies, automated intelligent legal agents, electronic legal institutions, computational models of legal texts. Ramifications for AI and Law in e-Commerce, automatic contracting and negotiation, digital rights management, and automated dispute resolution. Ramifications for AI and Law in e-governance, e-government, e-Democracy, and knowledge-based systems supporting public services, public dialogue and mediation. Intelligent computer-assisted instructional systems in law or ethics. Evaluation and auditing techniques for legal AI systems. Systemic problems in the construction and delivery of legal AI systems. Impact of AI on the law and legal institutions. Ethical issues concerning legal AI systems. In addition to original research contributions, the Journal will include a Book Review section, a series of Technology Reports describing existing and emerging products, applications and technologies, and a Research Notes section of occasional essays posing interesting and timely research challenges for the field of Artificial Intelligence and Law. Financial support for the Journal of Artificial Intelligence and Law is provided by the University of Pittsburgh School of Law.
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