Summary of Demystifying Legalese: An Automated Approach For Summarizing and Analyzing Overlaps in Privacy Policies and Terms Of Service, by Shikha Soneji et al.
Demystifying Legalese: An Automated Approach for Summarizing and Analyzing Overlaps in Privacy Policies and Terms of Service
by Shikha Soneji, Mitchell Hoesing, Sujay Koujalgi, Jonathan Dodge
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This research paper presents language models that generate automated summaries and scores for legal documents, aiming to improve user understanding and facilitate informed decisions. The authors compare transformer-based and conventional models during training on their dataset and find that RoBERTa performs better overall with a remarkable 0.74 F1-score. Leveraging RoBERTa, the best-performing model, they identify redundancies and potential guideline violations in GDPR-required documents, highlighting the need for stricter compliance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study helps people understand legal contracts by creating machines that can summarize them clearly and score their importance. The researchers compared different types of models to see which one worked best and found that RoBERTa was the top performer. They used this model to find parts of GDPR documents that are repetitive or unclear, showing why it’s important to follow these rules carefully. |
Keywords
» Artificial intelligence » F1 score » Transformer
