  Automated keyphrase extraction is a fundamental textual information
processing task concerned with the selection of representative phrases from a
document that summarize its content. This work presents a novel unsupervised
method for keyphrase extraction, whose main innovation is the use of local word
embeddings (in particular GloVe vectors), i.e., embeddings trained from the
single document under consideration. We argue that such local representation of
words and keyphrases are able to accurately capture their semantics in the
context of the document they are part of, and therefore can help in improving
keyphrase extraction quality. Empirical results offer evidence that indeed
local representations lead to better keyphrase extraction results compared to
both embeddings trained on very large third corpora or larger corpora
consisting of several documents of the same scientific field and to other
state-of-the-art unsupervised keyphrase extraction methods.
