Abstract
Generating keyphrases that summarize the
main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an
input document as well as determining the
number of keyphrases to generate, they still
suffer from the problem of generating too
few keyphrases. To address this problem,
we propose a reinforcement learning (RL)
approach for keyphrase generation, with an
adaptive reward function that encourages a
model to generate both sufficient and accurate keyphrases. Furthermore, we introduce a
new evaluation method that incorporates name
variations of the ground-truth keyphrases using the Wikipedia knowledge base. Thus, our
evaluation method can more robustly evaluate
the quality of predicted keyphrases. Extensive experiments on five real-world datasets of
different scales demonstrate that our RL approach consistently and significantly improves
the performance of the state-of-the-art generative models with both conventional and new
evaluation methods.