  Legal documents are indispensable in every country for legal practices and
serve as the primary source of information regarding previous cases and
employed statutes. In today's world, with an increasing number of judicial
cases, it is crucial to systematically categorize past cases into subgroups,
which can then be utilized for upcoming cases and practices. Our primary focus
in this endeavor was to annotate cases using topic modeling algorithms such as
Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and Bertopic
for a collection of lengthy legal documents from India and the UK. This step is
crucial for distinguishing the generated labels between the two countries,
highlighting the differences in the types of cases that arise in each
jurisdiction. Furthermore, an analysis of the timeline of cases from India was
conducted to discern the evolution of dominant topics over the years.
