Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning (2021)

Abstract: Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these KG evaluation metrics such as F_1@5 and F_1@M are only aware of the exact correctness of predictions on phrase-level and ignore the semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level F_1 score, edit distance, duplication, and prediction quantities. On the whole, the new framework includes two reward functions: the fine-grained evaluation score and the vanilla F_1 score. This framework helps the model identifying some partial match phrases which can be further optimized as the exact match ones. Experiments on KG benchmarks show that our proposed training framework outperforms the previous RL training frameworks among all evaluation scores. In addition, our method can effectively ease the synonym problem and generate a higher quality prediction. The source code is available at https://github.com/xuyige/FGRL4KG .

## related work in this section, we briefly introduce keyphrase generation models and evaluation metrics. ## conclusion in this paper, we utilize a two-stage reinforcement learning training framework with a fine-grained evaluation metric. we propose the f g-score or the continuous bert-score as the reward in the firststage training, which improves the generation quality on token-level and then beneficial to the secondstage training. experiments on kg benchmarks show the effectiveness of our proposed method, and then we also demonstrated the contribution of each module in the f g function. in addition, we evaluate the performance of bert-based scorer manually. in future work, we will consider improving the training of bert scorer's performance and making the two-stage rl training more effective.