  We present an approach to generating topics using a model trained only for
document title generation, with zero examples of topics given during training.
We leverage features that capture the relevance of a candidate span in a
document for the generation of a title for that document. The output is a
weighted collection of the phrases that are most relevant for describing the
document and distinguishing it within a corpus, without requiring access to the
rest of the corpus. We conducted a double-blind trial in which human annotators
scored the quality of our machine-generated topics along with original
human-written topics associated with news articles from The Guardian and The
Huffington Post. The results show that our zero-shot model generates topic
labels for news documents that are on average equal to or higher quality than
those written by humans, as judged by humans.
