  Keyword extraction is the task of identifying words (or multi-word
expressions) that best describe a given document and serve in news portals to
link articles of similar topics. In this work we develop and evaluate our
methods on four novel data sets covering less represented, morphologically-rich
languages in European news media industry (Croatian, Estonian, Latvian and
Russian). First, we perform evaluation of two supervised neural
transformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a
baseline TF-IDF based unsupervised approach. Next, we show that by combining
the keywords retrieved by both neural transformer based methods and extending
the final set of keywords with an unsupervised TF-IDF based technique, we can
drastically improve the recall of the system, making it appropriate to be used
as a recommendation system in the media house environment.
