  Authors' keyphrases assigned to scientific articles are essential for
recognizing content and topic aspects. Most of the proposed supervised and
unsupervised methods for keyphrase generation are unable to produce terms that
are valuable but do not appear in the text. In this paper, we explore the
possibility of considering the keyphrase string as an abstractive summary of
the title and the abstract. First, we collect, process and release a large
dataset of scientific paper metadata that contains 2.2 million records. Then we
experiment with popular text summarization neural architectures. Despite using
advanced deep learning models, large quantities of data and many days of
computation, our systematic evaluation on four test datasets reveals that the
explored text summarization methods could not produce better keyphrases than
the simpler unsupervised methods, or the existing supervised ones.
