  Privacy policy documents are long and verbose. A question answering (QA)
system can assist users in finding the information that is relevant and
important to them. Prior studies in this domain frame the QA task as retrieving
the most relevant text segment or a list of sentences from the policy document
given a question. On the contrary, we argue that providing users with a short
text span from policy documents reduces the burden of searching the target
information from a lengthy text segment. In this paper, we present PolicyQA, a
dataset that contains 25,017 reading comprehension style examples curated from
an existing corpus of 115 website privacy policies. PolicyQA provides 714
human-annotated questions written for a wide range of privacy practices. We
evaluate two existing neural QA models and perform rigorous analysis to reveal
the advantages and challenges offered by PolicyQA.
