推荐系统帮助法律专业人士找到相关文献来支持他们的案件。尽管它的重要性的专业，法律应用并没有反映在推荐系统和表示学习研究的最新进展。同时，法律推荐系统通常在没有任何公共基准数据集的情况下在小规模用户研究中进行评估。因此，这些研究的重现性有限。为了解决研究与实践之间的差距，我们探索了一套国家的最先进的文件表示方法检索语义相关的美国判例法的任务。我们评估基于文本的（例如，fastText、Transformers）、基于引用的（例如，DeepWalk，Poincaré）和混合方法。我们使用两种银标准对2，964份文件进行了注释，共比较了27种方法。银标准是从Open Case Book和Wikispecies新创建的，可以在开放许可下重复使用，以促进可重复性。我们的实验表明，来自平均fastText单词向量（在法律语料库上训练）的文档表示产生了最好的结果，紧随其后的是庞加莱引用嵌入。以混合方式组合fastText和Poincaré进一步改善了整体结果。除了整体性能外，我们还根据文档长度，引用计数和建议的覆盖范围分析了这些方法。我们在https://github.com/malteos/legal-document-similarity/上公开我们的源代码、模型和数据集。
英文摘要：
Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets. Thus, these studies have limited reproducibility. To address the gap between research and practice, we explore a set of state-of-the-art document representation methods for the task of retrieving semantically related US case law. We evaluate text-based (e.g., fastText, Transformers), citation-based (e.g., DeepWalk, Poincaré), and hybrid methods. We compare in total 27 methods using two silver standards with annotations for 2,964 documents. The silver standards are newly created from Open Case Book and Wikisource and can be reused under an open license facilitating reproducibility. Our experiments show that document representations from averaged fastText word vectors (trained on legal corpora) yield the best results, closely followed by Poincaré citation embeddings. Combining fastText and Poincaré in a hybrid manner further improves the overall result. Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations. We make our source code, models, and datasets publicly available at https://github.com/malteos/legal-document-similarity/.
Attentive Deep Neural Networks for Legal Document Retrieval
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；National Institute of Informatics（日本国立信息学研究所）；Posts and Telecommunications Institute of Technology（邮电技术学院）
CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation
Tsinghua University（清华大学）；Quan Cheng Laboratory（泉城实验室）；University of Waterloo（滑铁卢大学）；Xiaohongshu Inc（小红书公司）；Beijing University of Posts and Telecommunications（北京邮电大学）
Methods for Computing Legal Document Similarity: A Comparative Study
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；Tata Research Development and Design Centre (TRDDC)（塔塔研究开发与设计中心（TRDDC））；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；Cyber Security CRC（网络安全合作研究中心）
Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Technical University of Darmstadt（德意志联邦共和国达姆斯塔特技术大学）；University of Trento（特伦托大学）；University of Florence（佛罗伦萨大学）；University of Bologna（博洛尼亚大学）；European University Institute（欧洲大学研究所）
LawSum: A weakly supervised approach for Indian Legal Document Summarization
Dhirubhai Ambani Institute of Information and Communication Technology（迪鲁巴伊·安巴尼信息与通信技术学院）；Malaviya National Institute of Technology（马拉维亚国家理工学院）；Paramonic AI
Corpus for Automatic Structuring of Legal Documents
EkStep Foundation（EkStep基金会）；Thoughtworks Technologies India Pvt Ltd.（印度思特沃克技术私人有限公司）；Agami；Indian Institute of Technology Kanpur (IIT-K)（印度理工学院坎普尔分校（IIT-K））
Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej
Indian Institute of Technology Kanpur, India（印度理工学院坎浦分校）；University of Birmingham Dubai, UAE（迪拜大学）；Symbiosis Law School Pune, India（萨比欧法学院皮尔分校）；IISER Kolkata, India（印度科学学院科钦分校）
Semantic Segmentation of Legal Documents via Rhetorical Roles
Indian Institute of Technology Kanpur (IIT-K)（印度理工学院坎普尔分校）；West Bengal National University of Juridical Sciences (WBNUJS)（西孟加拉国立法律科学大学）；Indian Institute of Management Ranchi (IIM-R)（印度管理学院兰契分校）
Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院计算机科学系，胡志明市，越南）；Faculty of Information Science and Engineering, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院信息科学与工程系，胡志明市，越南）；Laboratory of Multimedia Communications, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院多媒体通信实验室，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）
Law to Binary Tree -- An Formal Interpretation of Legal Natural Language
National Institute of Informatics（国立信息学研究所）；The Institute of Statistical Mathematics (ISM)（统计数学研究所）；Vietnam Judicial Academy（越南司法学院）；Hanoi Law University（河内法律大学）；VINASECO JSC（VINASECO股份公司）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities
Universidade de Fortaleza（福塔莱萨大学）；Instituto Federal de Educação, Ciência e Tecnologia do Ceará（塞阿拉联邦教育、科学与技术学院）；Tribunal de Justiça do Estado do Ceará（塞阿拉州法院）
