The paper introduces an expert‑annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations under I.R.C. §368, marking the first tree‑structured argument corpus in this domain. Each legal passage is labeled with one of five functional categories—Rule, Analysis, Conclusion, Background Facts, and Procedural History—and can be linked into directed support trees. Experiments demonstrate that functional labels are learnable and that supervised fine‑tuning improves within‑case retrieval, though cross‑case generalization remains weak.
By Luis Brena, William Jurayj, Gregory Deyesu, Zaid Al-Huneidi, Andrew Blair-Stanek, Benjamin Van Durme
Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations...
Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory language. In this paper, we propose GCSR, a generative statute retrieval framework that reformulates statute retrieval as a sequence generation problem and internalizes statutory knowledge into a generative model.
arXiv:2608.22446v1 Announce Type: new
Abstract: Metaphors are figurative use of words for conceptual mapping. Metaphor detection in the legal context has been crucial as metaphors are persuasive juri...
By Bhumika Bhattacharyya, Shouvik Kumar Guha, Indranil Dutta
arXiv:2606. 06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing largely to the absence of expert-annotated corpora.
By Xi Xuan, Wenxin Zhang, Yufei Zhou, King-kui Sin, Chunyu Kit
LexIssue introduces a benchmark for identifying disputed legal issues in Chinese civil litigation, comprising 430 real‑world cases and 1,303 expert‑annotated issues. The dataset is built around a hierarchical schema that links free‑form issue descriptions to structured legal categories, enabling two complementary tasks: issue generation and issue classification. A retrieval‑augmented knowledge base covering 27 causes of action and 441 issue entries is provided, and experiments show that incorporating this knowledge consistently improves model performance on the tasks.
By Huiyuan Xie, Yuqin Huang, Zhicheng Hao, Yida Cai, Shaochun Wang, Zhenghao Liu, Yuxiao Ye
The paper introduces an automated pipeline that converts court decisions into legal commentaries for specific German Civil Code sections, using paragraph extraction, summarization, keyword clustering, and large language models to generate headings and citation-rich sections. The system processes 4,555 decisions from the German Federal Court of Justice, evaluates the output on relevance, heading match, citation faithfulness, cluster distinction, and logical ordering, and demonstrates that rapid, low-cost commentary generation is feasible while noting limitations due to source restrictions and legal reasoning norms.
By Max Prior, Niklas Wais, Matthias Grabmair
arXiv:2608. 08830v1 Announce Type: new Abstract: Legal Statute Prediction (LSP) involves automatically identifying relevant legal statutes given factual descriptions in legal documents, typically framed as a multi-label classification task within natural language processing and information retrieval research.
By Subinay Adhikary, Upal Bhattacharya, Vivek Kumar Singh, Anurag Sharma, Shubham Kumar Nigam, Suvasis Das, Shouvik Kumar Guha, Koustav Rudra, Kripabandhu Ghosh
arXiv:2608.03756v2 Announce Type: cross
Abstract: A common task in legal Information Retrieval (IR) is to find relevant legal sources from case-law collections. While legal practice often requires pi...
By Theresia Veronika Rampisela, Henrik Palmer Olsen, Giovanni Colavizza
arXiv:2607. 18825v1 Announce Type: cross Abstract: This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context.
By Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts and to enhance the accuracy and reliability of responses to legal questions.
arXiv:2607. 03325v1 Announce Type: cross Abstract: We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured XML representation grounded in the IRAC framework and the legal syllogism.
By Giovanni Piccioli, Alessia Fidelangeli, Piera Santin, Pierpaolo Vivo