  Topics generated by topic models are typically represented as list of terms.
To reduce the cognitive overhead of interpreting these topics for end-users, we
propose labelling a topic with a succinct phrase that summarises its theme or
idea. Using Wikipedia document titles as label candidates, we compute neural
embeddings for documents and words to select the most relevant labels for
topics. Compared to a state-of-the-art topic labelling system, our methodology
is simpler, more efficient, and finds better topic labels.
