  Modern models for text generation show state-of-the-art results in many
natural language processing tasks. In this work, we explore the effectiveness
of abstractive text summarization models for keyphrase selection. A list of
keyphrases is an important element of a text in databases and repositories of
electronic documents. In our experiments, abstractive text summarization models
fine-tuned for keyphrase generation show quite high results for a target text
corpus. However, in most cases, the zero-shot performance on other corpora and
domains is significantly lower. We investigate cross-domain limitations of
abstractive text summarization models for keyphrase generation. We present an
evaluation of the fine-tuned BART models for the keyphrase selection task
across six benchmark corpora for keyphrase extraction including scientific
texts from two domains and news texts. We explore the role of transfer learning
between different domains to improve the BART model performance on small text
corpora. Our experiments show that preliminary fine-tuning on out-of-domain
corpora can be effective under conditions of a limited number of samples.
