為加速勞資爭議訴訟相關裁判之審理進程，法院會依據聲請人與相對人兩造，即勞資雙方所有爭執之事項進行條列，是為「爭點列表」，並逐一對其進行審理。
然考量近年來勞資爭議相關訴訟有上升之趨勢，對一般大眾而言，查找具相似情事之裁判書並給出具可解釋性相似性理由之自動化方法尤為重要。
因此，本研究將結合傳統機器學習方法 (分群、邏輯迴歸、集成學習) 與深度學習方法 (S-BERT、大型語言模型、CNN、BiLSTM) ，建構一類似案件推薦系統，並對索引與推薦之案件標注不同指涉事項之對應相似段落，以期能減少一般大眾查找與篩選類似案件之負擔。
本研究以前沿研究之標記資料與裁判書資料基礎下，將預測準確率由70%提升至77%，但考慮到模型需具客觀能效之比較，我們亦在後續章節與其他類似案件推薦研究之架構進行能效對比。
本研究主要貢獻為：基於敘述句分群結果產生對應內容之微調（fine-tune）訓練S-BERT模型之資料與方法、以兩造主張段落及爭點列表進行類似案件推薦、僅以原告主張段落與根據Chatgpt產生之摘要進行類似案件推薦。
鑒於各級司法機關新收案件數量皆有上升之趨勢，本研究亦在無標記基礎下產生微調訓練S-BERT模型之資料與方法、使用ChatGPT抽取摘要之prompt，及相對通用之可解釋性標注與推薦架構 (僅需主張段落與該段落之摘要) ，以期將該方法推廣至其他類別之民事訴訟，乃至其他領域。

In order to speed up the adjudication process of labor litigation, the court will list all the disputes between the plaintiff and the defendant, that is, the disputes between the employer and employee, as a "list of disputes", and review them one by one.
However, considering that the number of lawsuits related to labor disputes is increasing these years, it is particularly important for general public to find automated methods that find judgments with similar situations and provide explainable reasons for the similarities.
Therefore, this research will combine traditional machine learning methods (clustering, logistic regression, ensemble learning) and deep learning methods (S-BERT, large language models, CNN, BiLSTM) to construct a similar case recommendation system, and perform indexing and recommendation cases are marked with corresponding similar paragraphs that refer to different matters, in order to reduce the burden on general public to find and screen similar cases.
Based on the label data and judgment data from cutting-edge research, this study increased the prediction accuracy from 70% to 77%. However, considering that the model needs to have objective performance comparisons, we will also recommend research with other similar cases in subsequent chapters. Architectures are compared for performance.
The main contributions of this study are: the materials and methods for training the S-BERT model based on fine-tuning the corresponding content generated by the clustering results of narrative sentences, recommending similar cases using two relay paragraphs and argument lists, and using only the plaintiff’s claim paragraph and summary generated based on ChatGPT for similar case recommendations.
In view of the increasing trend in the number of new cases received by judicial agencies at all levels, this study also produces data and methods for fine-tuning and training the S-BERT model on a label-free basis, prompts for extracting summaries using ChatGPT, and relatively universal interpretable annotations. and a recommended structure (only the claim paragraph and a summary of the paragraph are required), with a view to extending this method to other types of civil litigation and even other fields.

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