  Keyphrase extraction is the task of automatically selecting a small set of
phrases that best describe a given free text document. Supervised keyphrase
extraction requires large amounts of labeled training data and generalizes very
poorly outside the domain of the training data. At the same time, unsupervised
systems have poor accuracy, and often do not generalize well, as they require
the input document to belong to a larger corpus also given as input. Addressing
these drawbacks, in this paper, we tackle keyphrase extraction from single
documents with EmbedRank: a novel unsupervised method, that leverages sentence
embeddings. EmbedRank achieves higher F-scores than graph-based state of the
art systems on standard datasets and is suitable for real-time processing of
large amounts of Web data. With EmbedRank, we also explicitly increase coverage
and diversity among the selected keyphrases by introducing an embedding-based
maximal marginal relevance (MMR) for new phrases. A user study including over
200 votes showed that, although reducing the phrases' semantic overlap leads to
no gains in F-score, our high diversity selection is preferred by humans.
