Legal Reasoning Chains: Faithful Summarization via Explicit Argument Structure Extraction

While existing models (e.g., LexT5, Santosh et al. 2024) and benchmarks often focus on surface-level faithfulness and content selection, they rarely model the underlying logical structure of legal arguments. This idea proposes a summarization pipeline that first extracts explicit legal arguments, precedents, and reasoning chains (possibly using techniques from argument mining or legal-specific NLP) and then conditions summary generation on these structured representations. By making the model "show its work," this approach aims to reduce hallucinations (as observed in CaseSumm, Heddaya et al. 2024, where models often misrepresent case facts or precedents) and improve the traceability and verifiability of summaries. This diverges from most current LLM summarization methods by tightly coupling legal reasoning structure to summary output—potentially enabling explainable, auditable summaries that align with the unique demands of legal practice.

References:

1. CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions. Mourad Heddaya, Kyle MacMillan, Anup Malani, Hongyuan Mei, Chenhao Tan (2024). North American Chapter of the Association for Computational Linguistics.
2. LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English. Santosh T.Y.S.S, Cornelius Weiss, Matthias Grabmair (2024). NLLP.