  This paper addresses a critical gap in legal analytics by developing and
applying a novel taxonomy for topic modelling summary judgment cases in the
United Kingdom. Using a curated dataset of summary judgment cases, we use the
Large Language Model Claude 3 Opus to explore functional topics and trends. We
find that Claude 3 Opus correctly classified the topic with an accuracy of
87.10%. The analysis reveals distinct patterns in the application of summary
judgments across various legal domains. As case law in the United Kingdom is
not originally labelled with keywords or a topic filtering option, the findings
not only refine our understanding of the thematic underpinnings of summary
judgments but also illustrate the potential of combining traditional and
AI-driven approaches in legal classification. Therefore, this paper provides a
new and general taxonomy for UK law. The implications of this work serve as a
foundation for further research and policy discussions in the field of judicial
administration and computational legal research methodologies.
