Abstract:
A legal document is usually long and dense
requiring human effort to parse it. It also
contains significant amounts of jargon which
make deriving insights from it using existing
models a poor approach. This paper presents
the approaches undertaken to perform the
task of rhetorical role labelling on Indian
Court Judgements as part of SemEval Task 6:
understanding legal texts, shared subtask A
(Modi et al., 2023). We experiment with graph
based approaches like Graph Convolutional
Networks and Label Propagation Algorithm,
and transformer-based approaches including
variants of BERT to improve accuracy
scores on text classification of complex legal
documents.