A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs
Summary
Proposes a tree-of-thoughts inspired extractive-abstractive approach for legal case judgement summarization using LLMs, with experiments on DeepSeek and LLama showing improved summaries over extractive or abstractive methods alone.
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# A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs Source: [https://arxiv.org/abs/2606.28044](https://arxiv.org/abs/2606.28044) [View PDF](https://arxiv.org/pdf/2606.28044) > 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 extractive\-abstractive summarization\. Our experiments show that the proposed extractive\-abstractive prompt provides better summaries compared to other types of LLM prompts\. ## Submission history From: Aniket Deroy \[[view email](https://arxiv.org/show-email/0d185f74/2606.28044)\] **\[v1\]**Fri, 26 Jun 2026 12:46:27 UTC \(235 KB\)
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