  This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal
Argument Reasoning in Civil Procedure. To address this Binary Classification
task, which was daunting due to the complexity of the Legal Texts involved, we
propose a simple yet novel similarity and distance-based unsupervised approach
to generate labels. Further, we explore the Multi-level fusion of Legal-Bert
embeddings using ensemble features, including CNN, GRU, and LSTM. To address
the lengthy nature of Legal explanation in the dataset, we introduce T5-based
segment-wise summarization, which successfully retained crucial information,
enhancing the model's performance. Our unsupervised system witnessed a 20-point
increase in macro F1-score on the development set and a 10-point increase on
the test set, which is promising given its uncomplicated architecture.
