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LexAbSumm: Aspect-based Summarization of Legal Decisions

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arxiv 2404.00594 v1 pith:DCURC6PW submitted 2024-03-31 cs.CL

T.Y.S.S Santosh , Mahmoud Aly , Matthias Grabmair This is my paper · ORCID
classification cs.CL
keywords legalsummarizationaspect-basedlexabsummdecisionsdocumentsmodelssummaries
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Legal professionals frequently encounter long legal judgments that hold critical insights for their work. While recent advances have led to automated summarization solutions for legal documents, they typically provide generic summaries, which may not meet the diverse information needs of users. To address this gap, we introduce LexAbSumm, a novel dataset designed for aspect-based summarization of legal case decisions, sourced from the European Court of Human Rights jurisdiction. We evaluate several abstractive summarization models tailored for longer documents on LexAbSumm, revealing a challenge in conditioning these models to produce aspect-specific summaries. We release LexAbSum to facilitate research in aspect-based summarization for legal domain.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity

    cs.CL 2025-01 conditional novelty 5.0 of 10

    Diverse, influence-function-scored exemplar summaries retrieved with a DPP improve legal summarization over no-exemplar and similarity-only baselines on SuperSCOTUS and CivilSum, with modest and statistically partial gains.

  2. Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation

    cs.CL 2025-04 conditional novelty 4.0 of 10

    SARESG prunes documents to aspect-relevant sentences via embedding similarity before LLM summarization, reporting gains over selective-context baselines on three datasets.

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