Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization
Researchers have developed a new model combining BiGRU with a Kolmogorov-Arnold Network (KAN) block to improve the classification and summarization of legal documents. This approach addresses challenges like multilingualism, domain-specific language, and class imbalance, particularly in a low-resource setting using data from Bangladesh. The KAN-enhanced BiGRU model achieved 67.96% accuracy in classification and demonstrated promising results in summarization tasks, outperforming baseline methods. AI
IMPACT Introduces a novel architecture for low-resource legal document processing, potentially improving efficiency in multilingual legal contexts.