  Recently, the sequence-to-sequence models have made remarkable progress on
the task of keyphrase generation (KG) by concatenating multiple keyphrases in a
predefined order as a target sequence during training. However, the keyphrases
are inherently an unordered set rather than an ordered sequence. Imposing a
predefined order will introduce wrong bias during training, which can highly
penalize shifts in the order between keyphrases. In this work, we propose a new
training paradigm One2Set without predefining an order to concatenate the
keyphrases. To fit this paradigm, we propose a novel model that utilizes a
fixed set of learned control codes as conditions to generate a set of
keyphrases in parallel. To solve the problem that there is no correspondence
between each prediction and target during training, we propose a $K$-step
target assignment mechanism via bipartite matching, which greatly increases the
diversity and reduces the duplication ratio of generated keyphrases. The
experimental results on multiple benchmarks demonstrate that our approach
significantly outperforms the state-of-the-art methods.
