用KAN模块增强BiGRU，提升低资源多语种法律文本分类与摘要效果
Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization
- 将KAN网络嵌入BiGRU，捕捉法律文本长距离依赖关系
- 分类准确率67.96%，较基线提升10.62个百分点，摘要ROUGE-1达0.38
- 适合处理多语言、小样本的法律文本任务，尤其适用于非英语场景
本研究提出一种基于KAN的双向GRU模型，用于低资源多语种环境下法律文档的分类与摘要任务。针对领域语言特性、多语言混杂、上下文长依赖及类别不平衡问题，采用来自孟加拉国曼普特拉平台的孟加拉语、英语及转写孟加拉语法律文档数据集。分类任务使用带KAN模块的BiGRU模型，摘要部分采用注意力GRU结合KAN模型头。分类准确率为67.96%，F1得分为0.65；摘要任务的ROUGE-1、ROUGE-2和ROUGE-L得分分别为0.38、0.23和0.31。消融实验表明，引入KAN使分类准确率从57.34%提升至67.96%。该方法在多个基线（包括传统机器学习与预训练语言模型）上表现更优。
原文摘要 · Abstract (English)
This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resource multilingual setup. In order to tackle problems associated with domain language, the usage of different languages, long dependencies within context, and class imbalance, we employ the dataset composed of legal documents from Bangladesh and taken from Manupatra, which include Bengali, English, and transliterated Bengali languages. Our classification task involves BiGRU model, along with Kolmogorov-Arnold Network (KAN) module, while the summarization part utilizes attention-based GRU, combined with a KAN model head. Classification model yields 67.96% of accuracy and 0.65 F1 score; while ROUGE-1, ROUGE-2, and ROUGE-L measures for summarization yield 0.38, 0.23, and 0.31 F1 scores, correspondingly. Ablation study shows that the use of KAN increases classification accuracy from 57.34% to 67.96%. Moreover, our proposed technique is compared to several baselines, including classical ML algorithms and pretrained language models.
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