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arXiv · 2004.13956

Zero-shot topic generation

Oleg Vasilyev·Kathryn Evans·Anna Venancio-Marques·John Bohannon
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Abstract

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.

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Oleg Vasilyev, Kathryn Evans, Anna Venancio-Marques, John Bohannon. 2020-04-29. Zero-shot topic generation. https://arxiv.org/abs/2004.13956

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