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This research introduces AugAbEx, a novel pipeline that enhances legal document summarization by combining abstractive generation with extractive methods to create high-quality silver-standard datasets. The approach effectively addresses limitations in current models handling complex legal jargon and long-form judgments across seven English case-dataset benchmarks. By improving evaluation metrics for hybrid algorithms without relying on expensive human annotations, this work advances practical NLP tools specifically tailored for the legal domain.
AugAbEx: Bridging Abstractive and Extractive Legal Summarization
arXiv:2511.12290v2 Announce Type: replace Abstract: Automatic summarization of legal judgments liberates law professionals from heavy cognitive burden due to the complexity of the language, context-sensitive legal jargon, and the length of the document. Caveats of abstractive summarization for legal documents, revealed in recent studies, have impelled the development of newhybrid and extractive summarization methods, along with datasets in the legal domain. We propose an efficient and elegant pipeline (AugAbEx) to repurpose an existing large case summarization dataset with human-written gold-standard summaries by augmenting it with silver standard extractive summaries ensconcing experts' opinion. Availability of silver summaries along with gold standard human-written summaries will bolster development and evaluation of novel hybrid and extractive case-summarization algorithms in the legal domain. We thoroughly scrutinize the augmented extractive summaries in structural, lexical, and semantic dimensions, within a domain specific framework, to ensure quality. Extensive experiments and statistical test on seven English legal case-summarization datasets demonstrate that the silver standard extractive summaries produced by the proposed pipeline score well across all evaluation dimensions. Comparison of AugAbEx with two baselines and three current state-of-the-art methods reveals that it outperforms all competing methods, and the quality of the summaries produced by the proposed pipeline is superior
Abstract:Automatic summarization of legal judgments liberates law professionals from heavy cognitive burden due to the complexity of the language, context-sensitive legal jargon, and the length of the document. Caveats of abstractive summarization for legal documents, revealed in recent studies, have impelled the development of newhybrid and extractive summarization methods, along with datasets in the legal domain. We propose an efficient and elegant pipeline (AugAbEx) to repurpose an existing large case summarization dataset with human-written gold-standard summaries by augmenting it with silver standard extractive summaries ensconcing experts' opinion. Availability of silver summaries along with gold standard human-written summaries will bolster development and evaluation of novel hybrid and extractive case-summarization algorithms in the legal domain. We thoroughly scrutinize the augmented extractive summaries in structural, lexical, and semantic dimensions, within a domain specific framework, to ensure quality. Extensive experiments and statistical test on seven English legal case-summarization datasets demonstrate that the silver standard extractive summaries produced by the proposed pipeline score well across all evaluation dimensions. Comparison of AugAbEx with two baselines and three current state-of-the-art methods reveals that it outperforms all competing methods, and the quality of the summaries produced by the proposed pipeline is superior
Computation and Language (cs.CL)
arXiv:2511.12290 [cs.CL]
(or arXiv:2511.12290v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2511.12290 Focus to learn more arXiv-issued DOI via DataCite
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