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.

通过使用受控抽象化摘要方法，我们提出了一种增强政策文件可解释性和可读性的方法，该方法强制要求生成的摘要中包括关键的与隐私有关的实体（如数据和媒介）以及组织收集这些实体的依据（如目标和原因）。我们开发了一个标有隐私相关实体标签的政策文件摘要数据集 PD-Sum。通过基于跨度的实体提取模型 EROS 以及近端策略优化（PPO）来控制摘要的信息内容，比较结果显示相对于各种基准模型，我们提出的模型 EROS 能够得到鼓舞人心的改进。此外，我们提供了定性和人工评估以证明 EROS 的有效性。