European Union’s Legislative Proposals Clustering Based on Multiple Hidden Layers Representation

Recently, there are opportunities and challenges for both legal practitioners and AI researchers due to the abundance of legal documents that are available in digital form. This development requires a lot of assistance and work for presenting this content in a helpful and distributed manner, which could have the potential to offer legal professionals many advantages. Therefore, we propose in this work an approach to cluster legislative procedures of the European Parliament based on the topics obtained from Kmeans algorithm using encoders and decoders of Pretrained Large Language Models (PLMs). The main objective of this method is to arrange legislative procedures according to their policy areas.