  Keyphrases are crucial for searching and systematizing scholarly documents.
Most current methods for keyphrase extraction are aimed at the extraction of
the most significant words in the text. But in practice, the list of keyphrases
often includes words that do not appear in the text explicitly. In this case,
the list of keyphrases represents an abstractive summary of the source text. In
this paper, we experiment with popular transformer-based models for abstractive
text summarization using four benchmark datasets for keyphrase extraction. We
compare the results obtained with the results of common unsupervised and
supervised methods for keyphrase extraction. Our evaluation shows that
summarization models are quite effective in generating keyphrases in the terms
of the full-match F1-score and BERTScore. However, they produce a lot of words
that are absent in the author's list of keyphrases, which makes summarization
models ineffective in terms of ROUGE-1. We also investigate several ordering
strategies to concatenate target keyphrases. The results showed that the choice
of strategy affects the performance of keyphrase generation.
