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.

本文总结了团队 SCaLAR 在《SemEval-2024 任务 5：民事诉讼中的法律论证》方面的工作。我们提出了一种简单而又新颖的基于相似度和距离的无监督方法来生成标签，以解决该二元分类任务。此外，我们通过使用 CNN、GRU 和 LSTM 等集成特征和多级融合的 Legal-Bert 嵌入来探索解决法律文本复杂性的问题。为了解决数据集中冗长的法律解释，我们引入了基于 T5 的分段摘要，成功保留了关键信息，提高了模型的性能。我们的无监督系统在开发集上的宏 F1 得分增加了 20 个百分点，在测试集上增加了 10 个百分点，这是一个令人鼓舞的结果，考虑到其简单的架构。