Challenge: Existing tools to interpret privacy policies have been used to understand them but there is a lack of large privacy policy corpora to simplify the process.
Approach: They propose to use a corpus of 1,005,380 English language privacy policies collected from the web to create semi-supervised and unsupervised models to interpret and simplify privacy policies.
Outcome: The proposed model outperforms all other publicly available privacy policy corpora and is ten times larger than the next largest public collection of privacy policies combined.

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Creation and Analysis of an International Corpus of Privacy Laws (2024.lrec-main)

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Challenge: a corpus of 1,043 privacy laws, regulations, and guidelines covers 183 jurisdictions . prior efforts to study privacy law in the form of privacy policies have lacked a large-scale collection .
Approach: They propose a corpus of 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions.
Outcome: The Privacy Law Corpus covers 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions.
APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation (2026.acl-long)

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Challenge: a lack of high-quality English privacy policy corpus optimized for legal clarity and readability is limiting translation of privacy policies . 139 privacy policies are often considered "incomprehensible" due to technical jargon, legal language, and convoluted grammatical structures.
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Outcome: The proposed framework outperforms large language models in terms of readability and accuracy.
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives (D19-1)

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Challenge: Privacy policies are long and complex documents that are difficult for users to read and understand.
Approach: They present a corpus of 1750 questions about privacy policies of mobile applications and over 3500 expert annotations of relevant answers.
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Building a Long Text Privacy Policy Corpus with Multi-Class Labels (2025.acl-long)

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Challenge: Legal text is susceptible to multiple valid, conflicting interpretations, and indeterminacy, interdependence between clauses, meaningful silence, and implications of legal defaults.
Approach: They propose to annotate privacy policies from 149 firms using a hand-coded dataset that captures key challenges peculiar to legal language.
Outcome: The proposed dataset includes privacy policies from 149 firms and includes materials incorporated by reference.
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)

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Challenge: generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation.
Approach: They propose a privacy evaluation benchmark to quantify the privacy leakage of language models.
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A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus (2022.lrec-1)

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Challenge: With the introduction of new privacy regulations, disclosures made by the same organization are not always the same in different languages.
Approach: They propose a language annotation scheme to capture nuances of two new privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA.
Outcome: The proposed method captures the nuances of two new privacy regulations and compares them to a corpus of 64 privacy policies in English and 91 in German with manual annotations for 8K and 19K fine-grained data practices.
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English (2023.acl-short)

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Challenge: Existing efforts to understand privacy policies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices.
Approach: They propose a privacy policy language understanding evaluation benchmark to evaluate the understanding of privacy policies across multiple tasks.
Outcome: The proposed framework improves the understanding of privacy policies across multiple tasks.
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? (2021.acl-long)

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Challenge: Privacy policies are long and complex documents that are difficult for users to read and comprehend.
Approach: They propose language technologies to help users reclaim control over their privacy . they highlight many remaining opportunities to develop more precise or nuanced language technologies .
Outcome: The proposed language technologies can address the privacy information gap . they can be more precise or nuanced in the way they use the text of privacy policies.
PolicyQA: A Reading Comprehension Dataset for Privacy Policies (2020.findings-emnlp)

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Challenge: Privacy policy documents are long and verbose. Hence, a question answering system can help users find the information that is relevant and important to them.
Approach: They propose to provide users with a short text span from policy documents to search for answers from a long text segment.
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021.emnlp-main)

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Challenge: Large text corpora are often introduced with minimal documentation . documenting collection process, composition, intended uses, and other are key for structured, task-specific datasets.
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