Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text
<|reference_start|>Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text: 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.<|reference_end|>
@article{prabhu2024towards,
    title={Towards Unsupervised Question Answering System with Multi-level
  Summarization for Legal Text},
    author={M Manvith Prabhu, Haricharana Srinivasa, Anand Kumar M},
    journal={In Proceedings of the 18th International Workshop on Semantic
  Evaluation (SemEval 2024), pages 193 to 199, Mexico City, Mexico. Association
  for Computational Linguistics},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13107},
    primaryClass={cs.CL cs.CY cs.LG}
}