  Legal Judgment Prediction (LJP) is a judicial assistance system that
recommends the legal components such as applicable statues, prison term and
penalty term by analyzing the given input case document. Indian legal system is
in the need of technical assistance such as artificial intelligence to solve
the crores of pending cases in various courts for years and its being increased
day to day. Most of the existing Indian models did not adequately concentrate
on the semantics embedded in the fact description (FD) that impacts the
decision. The proposed semantic extraction based LJP (SLJP) model provides the
advantages of pretrained transformers for complex unstructured legal case
document understanding and to generate embeddings. The model draws the in-depth
semantics of the given FD at multiple levels i.e., chunk and case document
level by following the divide and conquer approach. It creates the concise view
of the given fact description using the extracted semantics as per the original
court case document structure and predicts judgment using attention mechanism.
We tested the model performance on two available Indian datasets Indian Legal
Documents corpus (ILDC) and Indian Legal Statue Identification (ILSI) and got
promising results. Also shown the highest performance and less performance
degradation for increased epochs than base models on ILDC dataset.
