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.

通过对领域之间的文本进行预训练和微调，我们探索了抽象文本摘要模型在关键词选择任务中的效果，并发现在样本数量有限的情况下，领域间的转移学习可以提高模型性能。