PL-CA applies parametric RAG with LoRA to Chinese legal tasks and presents a 2,580-instance expert-annotated benchmark, claiming improved performance and lower context overhead than vanilla RAG.
An Evaluation Framework for Legal Document Summarization
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abstract
A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is important to summarize these documents, and ensure that summaries contain phrases with intent matching the category of the case. To the best of our knowledge, there is no evaluation metric that evaluates a summary based on its intent. We propose an automated intent-based summarization metric, which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. in terms of human satisfaction. We also curate a dataset by annotating intent phrases in legal documents, and show a proof of concept as to how this system can be automated. Additionally, all the code and data to generate reproducible results is available on Github.
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cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
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PL-CA: A Parametric Legal Case Augmentation Framework
PL-CA applies parametric RAG with LoRA to Chinese legal tasks and presents a 2,580-instance expert-annotated benchmark, claiming improved performance and lower context overhead than vanilla RAG.