  Topic modelling is a popular unsupervised method for identifying the
underlying themes in document collections that has many applications in
information retrieval. A topic is usually represented by a list of terms ranked
by their probability but, since these can be difficult to interpret, various
approaches have been developed to assign descriptive labels to topics. Previous
work on the automatic assignment of labels to topics has relied on a two-stage
approach: (1) candidate labels are retrieved from a large pool (e.g. Wikipedia
article titles); and then (2) re-ranked based on their semantic similarity to
the topic terms. However, these extractive approaches can only assign candidate
labels from a restricted set that may not include any suitable ones. This paper
proposes using a sequence-to-sequence neural-based approach to generate labels
that does not suffer from this limitation. The model is trained over a new
large synthetic dataset created using distant supervision. The method is
evaluated by comparing the labels it generates to ones rated by humans.
