A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs
arXiv:2606.28044v1 Announce Type: new Abstract: In recent times, Large Language Models (LLMs) are increasingly being used for legal case judgement summarization. Most prior works have tried traditional extractive and abstractive summarization of case judgements. However, hybrid or extractive-abstractive techniques have not been explored much. In this work, we propose a novel tree-of-thoughts inspired extractive-abstractive summarization approach for legal judgement summarization. We conduct experiments using two popular LLMs, DeepSeek and LLama, and compare among extractive, abstractive and ex
The rapid advancement and accessibility of LLMs are enabling their application to highly specialized and complex domains like legal reasoning and summarization.
Improved AI capabilities in legal summarization can enhance efficiency, reduce costs, and increase access to legal information, posing both opportunities and challenges for the legal sector.
The methodology for summarizing complex legal documents is shifting from purely traditional techniques to more sophisticated, hybrid AI-driven approaches, promising greater accuracy and nuance.
- · Legal Tech Companies
- · Law Firms (adopting AI tools)
- · AI Researchers & Developers
- · DeepSeek
- · LLaMA (Meta)
- · Traditional legal research services
- · Clerical legal workers (some tasks)
LLMs demonstrate enhanced capability in specific legal reasoning tasks, particularly summarization.
This improvement could lead to the automation of more advanced legal research and document review processes.
The broader integration of AI into legal practice may redefine the necessary skill sets for legal professionals and the overall structure of legal services.
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