  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.
