  Keyword extraction is the task of retrieving words that are essential to the
content of a given document. Researchers proposed various approaches to tackle
this problem. At the top-most level, approaches are divided into ones that
require training - supervised and ones that do not - unsupervised. In this
study, we are interested in settings, where for a language under investigation,
no training data is available. More specifically, we explore whether pretrained
multilingual language models can be employed for zero-shot cross-lingual
keyword extraction on low-resource languages with limited or no available
labeled training data and whether they outperform state-of-the-art unsupervised
keyword extractors. The comparison is conducted on six news article datasets
covering two high-resource languages, English and Russian, and four
low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find
that the pretrained models fine-tuned on a multilingual corpus covering
languages that do not appear in the test set (i.e. in a zero-shot setting),
consistently outscore unsupervised models in all six languages.
