  This paper tackles the problem of automatically labelling sentiment-bearing
topics with descriptive sentence labels. We propose two approaches to the
problem, one extractive and the other abstractive. Both approaches rely on a
novel mechanism to automatically learn the relevance of each sentence in a
corpus to sentiment-bearing topics extracted from that corpus. The extractive
approach uses a sentence ranking algorithm for label selection which for the
first time jointly optimises topic--sentence relevance as well as
aspect--sentiment co-coverage. The abstractive approach instead addresses
aspect--sentiment co-coverage by using sentence fusion to generate a sentential
label that includes relevant content from multiple sentences. To our knowledge,
we are the first to study the problem of labelling sentiment-bearing topics.
Our experimental results on three real-world datasets show that both the
extractive and abstractive approaches outperform four strong baselines in terms
of facilitating topic understanding and interpretation. In addition, when
comparing extractive and abstractive labels, our evaluation shows that our best
performing abstractive method is able to provide more topic information
coverage in fewer words, at the cost of generating less grammatical labels than
the extractive method. We conclude that abstractive methods can effectively
synthesise the rich information contained in sentiment-bearing topics.
