Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach
This work addresses the problem of making privacy policies more accessible and personalized for users, though it is incremental by building on existing summarization techniques.
The paper tackles the challenge of answering user questions about privacy policies by proposing a query-guided extractive summarization approach that paraphrases user queries to match legal language and uses a content scoring module to find relevant information, achieving an 89% answer rate on the privacyQA dataset.
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.