The complexities of legalese in terms and policy documents can bind
individuals to contracts they do not fully comprehend, potentially leading to
uninformed data sharing. Our work seeks to alleviate this issue by developing
language models that provide automated, accessible summaries and scores for
such documents, aiming to enhance user understanding and facilitate informed
decisions. We compared transformer-based and conventional models during
training on our dataset, and RoBERTa performed better overall with a remarkable
0.74 F1-score. Leveraging our best-performing model, RoBERTa, we highlighted
redundancies and potential guideline violations by identifying overlaps in
GDPR-required documents, underscoring the necessity for stricter GDPR
compliance.

我们的研究致力于通过开发语言模型来为合同和政策文件提供自动化、易于理解的摘要和分数，以增强用户理解，促进知情决策。我们在数据集上比较了基于 Transformer 和传统模型，RoBERTa 整体表现更好，达到了 0.74 的 F1 分数。利用我们表现最佳的模型 RoBERTa，我们通过识别 GDPR 文件的重叠部分突出了冗余和潜在的指导方针违规情况，强调了对更严格 GDPR 合规性的必要性。