  Category systems are central components of knowledge bases, as they provide a
hierarchical grouping of semantically related concepts and entities. They are a
unique and valuable resource that is utilized in a broad range of information
access tasks. To aid knowledge editors in the manual process of expanding a
category system, this paper presents a method of generating categories for sets
of entities. First, we employ neural abstractive summarization models to
generate candidate categories. Next, the location within the hierarchy is
identified for each candidate. Finally, structure-, content-, and
hierarchy-based features are used to rank candidates to identify by the most
promising ones (measured in terms of specificity, hierarchy, and importance).
We develop a test collection based on Wikipedia categories and demonstrate the
effectiveness of the proposed approach.
