  This study mainly investigates two common decoding problems in neural
keyphrase generation: sequence length bias and beam diversity. To tackle the
problems, we introduce a beam search decoding strategy based on word-level and
ngram-level reward function to constrain and refine Seq2Seq inference at test
time. Results show that our simple proposal can overcome the algorithm bias to
shorter and nearly identical sequences, resulting in a significant improvement
of the decoding performance on generating keyphrases that are present and
absent in source text.
