Visible to the public Automatic Summarization of Privacy Policies Using Ensemble Learning

TitleAutomatic Summarization of Privacy Policies Using Ensemble Learning
Publication TypeConference Paper
Year of Publication2016
AuthorsTomuro, Noriko, Lytinen, Steven, Hornsburg, Kurt
Conference NameProceedings of the Sixth ACM Conference on Data and Application Security and Privacy
Date PublishedMarch 2016
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-3935-3
Keywordscomposability, compositionality, Computational Intelligence, cryptography, expert systems, human factors, machine learning, natural language processing, privacy, privacy policy, pubcrawl, Scalability, security

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.

Citation Keytomuro_automatic_2016