Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization
Quick Take
This study presents a KAN-enhanced BiGRU model for legal document classification and summarization, achieving 67.96% accuracy and improved performance over classical ML algorithms. The model addresses multilingual challenges using a dataset from Bangladesh, demonstrating significant gains in classification accuracy with KAN integration.
Key Points
- KAN integration boosts BiGRU classification accuracy from 57.34% to 67.96%.
- Model utilizes a dataset of multilingual legal documents from Bangladesh.
- Summarization performance includes ROUGE-1, ROUGE-2, and ROUGE-L scores.
- Attention-based GRU is employed for the summarization task.
- Study compares the proposed model against classical ML and pretrained models.
Article Excerpt
From source RSS / original summaryarXiv:2606. 00116v1 Announce Type: new Abstract: 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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