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On Imbalance in Case Types: Evaluating and Enhancing PLMs for Criminal Court View Generation.

Yuquan Le, Zheng Xiao, Yan Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |November 3, 2025
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    New metrics evaluate criminal court view generation (CCVG) by focusing on case types, addressing limitations of traditional averaged scores. This ensures fairer assessment and identifies performance imbalances in legal text summarization models.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Law and Technology

    Background:

    • The criminal court view generation (CCVG) task requires generating concise, coherent summaries of legal facts for verdicts.
    • Current evaluation metrics like ROUGE, BLEU, and BERTSCORE average performance across all samples, failing to account for variations between different case types.
    • This averaging leads to unfair assessments and overlooks performance disparities across diverse legal scenarios.

    Purpose of the Study:

    • To address the limitations of traditional sample-averaged metrics in CCVG.
    • To introduce novel evaluation metrics that provide fair and balanced performance assessment across different case types.
    • To propose a new framework to improve CCVG model performance equitably across case types.

    Main Methods:

    • Proposed two novel case-type-oriented evaluation metrics: Case-type-oriented Text Generation (CTG) and Case-type-oriented Imbalance Performance (CIP).
    • CTG assigns equal weight to each case type for fair assessment.
    • CIP measures performance imbalance by comparing individual case type performance to overall performance, supported by three elucidating theorems.

    Main Results:

    • The proposed CTG and CIP metrics offer a fair and nuanced evaluation of CCVG models, unlike traditional averaged metrics.
    • CIP effectively identifies and quantifies performance imbalances across different legal case types.
    • The charge-guided encoder-decoder (CGED) framework demonstrates improved and fair performance across various case types in pretrained language models.

    Conclusions:

    • Novel case-type-oriented metrics (CTG and CIP) provide a more equitable and insightful evaluation for criminal court view generation.
    • These metrics are crucial for understanding and mitigating performance disparities in legal text summarization models.
    • The CGED framework offers a practical solution for enhancing the fairness and effectiveness of CCVG systems.