mlsanityData to 14 Oct 2026

Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach

arXiv 2109.14638 ↗First posted 29 Sep 2021cs.CL cs.CY cs.IRcited by 5

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Abstract (arXiv)

Existing work on making privacy policies accessible has explored new presentation forms such as color-coding based on the risk factors or summarization to assist users with conscious agreement. To facilitate a more personalized interaction with the policies, in this work, we propose an automated privacy policy question answering assistant that extracts a summary in response to the input user query. This is a challenging task because users articulate their privacy-related questions in a very different language than the legal language of the policy, making it difficult for the system to understand their inquiry. Moreover, existing annotated data in this domain are limited. We address these problems by paraphrasing to bring the style and language of the user's question closer to the language of privacy policies. Our content scoring module uses the existing in-domain data to find relevant information in the policy and incorporates it in a summary. Our pipeline is able to find an answer for 89% of the user queries in the privacyQA dataset.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 29 Sep 2021.

Builds on

17 of 41 references.

References · 17

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5 more
  1. 2017Query expansion techniques for information retrieval: A surveycited by 44
  2. 2020PolicyQA: A Reading Comprehension Dataset for Privacy Policiescited by 34
  3. 2019Plain English Summarization of Contractscited by 28
  4. 2019Mobile App Privacy in Software Engineering Research: A Systematic Mapping Studycited by 3
  5. 2021Fairness-aware Summarization for Justified Decision-Makingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

5 papers.