  In the Indian court system, pending cases have long been a problem. There are
more than 4 crore cases outstanding. Manually summarising hundreds of documents
is a time-consuming and tedious task for legal stakeholders. Many
state-of-the-art models for text summarization have emerged as machine learning
has progressed. Domain-independent models don't do well with legal texts, and
fine-tuning those models for the Indian Legal System is problematic due to a
lack of publicly available datasets. To improve the performance of
domain-independent models, the authors have proposed a methodology for
normalising legal texts in the Indian context. The authors experimented with
two state-of-the-art domain-independent models for legal text summarization,
namely BART and PEGASUS. BART and PEGASUS are put through their paces in terms
of extractive and abstractive summarization to understand the effectiveness of
the text normalisation approach. Summarised texts are evaluated by domain
experts on multiple parameters and using ROUGE metrics. It shows the proposed
text normalisation approach is effective in legal texts with domain-independent
models.
