Conversational information-seeking (CIS) is an emerging paradigm for
knowledge acquisition and exploratory search. Traditional web search interfaces
enable easy exploration of entities, but this is limited in conversational
settings due to the limited-bandwidth interface. This paper explore ways to
rewrite answers in CIS, so that users can understand them without having to
resort to external services or sources. Specifically, we focus on salient
entities -- entities that are central to understanding the answer. As our first
contribution, we create a dataset of conversations annotated with entities for
saliency. Our analysis of the collected data reveals that the majority of
answers contain salient entities. As our second contribution, we propose two
answer rewriting strategies aimed at improving the overall user experience in
CIS. One approach expands answers with inline definitions of salient entities,
making the answer self-contained. The other approach complements answers with
follow-up questions, offering users the possibility to learn more about
specific entities. Results of a crowdsourcing-based study indicate that
rewritten answers are clearly preferred over the original ones. We also find
that inline definitions tend to be favored over follow-up questions, but this
choice is highly subjective, thereby providing a promising future direction for
personalization.

对话式信息获取是一种新兴的知识获取和探索性搜索范式，本论文旨在探索对话式信息获取中的答案重写方法，使用户能够理解答案而无需借助外部服务或来源。通过创建带有突出实体注释的对话数据集，该研究发现大多数答案包含重要实体。提出了两种旨在改善对话式信息获取中用户体验的答案重写策略，其中一种方法是通过内联定义重写答案，使答案在自身中包含所述重要实体的定义；另一种方法是通过补充后续问题来补充答案，使用户可以进一步了解特定实体。众包研究结果表明，重写后的答案明显优于原始答案。同时发现内联定义往往优于后续问题，但这种选择高度主观，这为个性化提供了一个有希望的未来方向。