Automatic Summarization of Privacy Policies Using Ensemble Learning
Title | Automatic Summarization of Privacy Policies Using Ensemble Learning |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Tomuro, Noriko, Lytinen, Steven, Hornsburg, Kurt |
Conference Name | Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy |
Date Published | March 2016 |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-3935-3 |
Keywords | composability, compositionality, Computational Intelligence, cryptography, expert systems, human factors, machine learning, natural language processing, privacy, privacy policy, pubcrawl, Scalability, security |
Abstract | When customers purchase a product or sign up for service from a company, they often are required to agree to a Privacy Policy or Terms of Service agreement. Many of these policies are lengthy, and a typical customer agrees to them without reading them carefully if at all. To address this problem, we have developed a prototype automatic text summarization system which is specifically designed for privacy policies. Our system generates a summary of a policy statement by identifying important sentences from the statement, categorizing these sentences by which of 5 "statement categories" the sentence addresses, and displaying to a user a list of the sentences which match each category. Our system incorporates keywords identified by a human domain expert and rules that were obtained by machine learning, and they are combined in an ensemble architecture. We have tested our system on a sample corpus of privacy statements, and preliminary results are promising. |
URL | https://dl.acm.org/doi/10.1145/2857705.2857741 |
DOI | 10.1145/2857705.2857741 |
Citation Key | tomuro_automatic_2016 |