We introduce a new task called *entity-centric question generation* (ECQG),
motivated by real-world applications such as topic-specific learning, assisted
reading, and fact-checking. The task aims to generate questions from an entity
perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE
with two novel modules: content focusing and question verification. The content
focusing module first identifies a focus as "what to ask" to form draft
questions, and the question verification module refines the questions
afterwards by verifying the answerability. We also construct a large-scale
open-domain dataset from SQuAD to support this task. Our extensive experiments
demonstrate that GenCONE significantly and consistently outperforms various
baselines, and two modules are effective and complementary in generating
high-quality questions.

我们引入一个称为 “实体中心问题生成”（ECQG）的新任务，旨在从实体的角度生成问题。为了解决 ECQG，我们提出了一个连贯的基于 PLM 的框架 GenCONE，其中包含两个新颖的模块：内容聚焦和问题验证。我们还构建了一个大规模的开放领域数据集，以支持这个任务。我们的大量实验证明，GenCONE 在生成高质量问题方面显著且一致地优于各种基线方法，而两个模块在此过程中是有效且互补的。