SubmissionNumber#=%=#82
FinalPaperTitle#=%=#ignore at SemEval-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Binjie Sun
JobTitle#==#
Organization#==#School of Information Science and Engineering Yunnan University
Abstract#==#This paper describes our work for SemEval-2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. After analyzing the task requirements and the training dataset, we used data augmentation, adopted the large model GPT for summary generation, and added supervised contrastive learning to the basic BERT model. Our system achieved an F1 score of 0.551, ranking 14th in the competition leaderboard. Our system achieves an F1 score improvement of 0.1241 over the official baseline model.
Author{1}{Firstname}#=%=#Binjie
Author{1}{Lastname}#=%=#Sun
Author{1}{Username}#=%=#ignore
Author{1}{Email}#=%=#sunbinjie@stu.ynu.edu.cn
Author{1}{Affiliation}#=%=#Yunnan University
Author{2}{Firstname}#=%=#Xiaobing
Author{2}{Lastname}#=%=#Zhou
Author{2}{Username}#=%=#zhouxb
Author{2}{Email}#=%=#zhouxb@ynu.edu.cn
Author{2}{Affiliation}#=%=#Yunnan University

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