  In recent years, a number of keyphrase generation (KPG) approaches were
proposed consisting of complex model architectures, dedicated training
paradigms and decoding strategies. In this work, we opt for simplicity and show
how a commonly used seq2seq language model, BART, can be easily adapted to
generate keyphrases from the text in a single batch computation using a simple
training procedure. Empirical results on five benchmarks show that our approach
is as good as the existing state-of-the-art KPG systems, but using a much
simpler and easy to deploy framework.
