  Privacy policy documents have a crucial role in educating individuals about
the collection, usage, and protection of users' personal data by organizations.
However, they are notorious for their lengthy, complex, and convoluted language
especially involving privacy-related entities. Hence, they pose a significant
challenge to users who attempt to comprehend organization's data usage policy.
In this paper, we propose to enhance the interpretability and readability of
policy documents by using controlled abstractive summarization -- we enforce
the generated summaries to include critical privacy-related entities (e.g.,
data and medium) and organization's rationale (e.g.,target and reason) in
collecting those entities. To achieve this, we develop PD-Sum, a
policy-document summarization dataset with marked privacy-related entity
labels. Our proposed model, EROS, identifies critical entities through a
span-based entity extraction model and employs them to control the information
content of the summaries using proximal policy optimization (PPO). Comparison
shows encouraging improvement over various baselines. Furthermore, we furnish
qualitative and human evaluations to establish the efficacy of EROS.
