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Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

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arxiv 2210.07544 v1 pith:64SM7RN6 submitted 2022-10-14 cs.CL cs.IR

Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

Abhay Shukla , Paheli Bhattacharya , Soham Poddar , Rajdeep Mukherjee , Kripabandhu Ghosh , Pawan Goyal , Saptarshi Ghosh This is my paper
classification cs.CL cs.IR
keywords summarizationlegalabstractivecasedocumentdocumentsextractiveanalyses
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer-based abstractive summarization models have restrictions on the number of input tokens, and legal documents are known to be very long. Also, it is an open question on how best to evaluate legal case document summarization systems. In this paper, we carry out extensive experiments with several extractive and abstractive summarization methods (both supervised and unsupervised) over three legal summarization datasets that we have developed. Our analyses, that includes evaluation by law practitioners, lead to several interesting insights on legal summarization in specific and long document summarization in general.

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Cited by 3 Pith papers

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  1. Gavel: Agent Meets Checklist for Evaluating LLMs on Long-Context Legal Summarization

    cs.CL 2026-01 conditional novelty 6.0

    Gavel uses a 26-item checklist, residual-fact, and style scoring to show that top LLMs cover only about half the key content in long legal case summaries and omit information more often than they invent it.

  2. LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents

    cs.AI 2025-09 reject novelty 5.0

    On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...

  3. Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization

    cs.CL 2025-09 reject novelty 5.0

    PEFT adapters trained on high-resource summarization domains can improve Llama-3-8B's summaries on unseen domains, but the reported gains are weakened by test-set selection and missing significance tests.