# Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards

## TL;DR

This paper introduces a reinforcement learning approach with an adaptive reward function for keyphrase generation, improving the number and accuracy of predicted keyphrases, and proposes a new evaluation method using Wikipedia for more robust assessment.

## Contribution

It presents a novel RL-based method with adaptive rewards for better keyphrase generation and a new evaluation technique leveraging Wikipedia to handle name variations.

## Key findings

- Significant performance improvements over state-of-the-art models.
- Consistent results across five diverse datasets.
- Enhanced evaluation robustness with Wikipedia-based metrics.

## 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.