Keyphrase extraction (KPE) is an important task in Natural Language Processing for many scenarios, which aims to extract keyphrases that are present in a given document. Many existing supervised methods treat KPE as sequential labeling, span-level classification, or generative tasks. However, these methods lack the ability to utilize keyphrase information, which may result in biased results. In this study, we propose Diff-KPE, which leverages the supervised Variational Information Bottleneck (VIB) to guide the text diffusion process for generating enhanced keyphrase representations. Diff-KPE first generates the desired keyphrase embeddings conditioned on the entire document and then injects the generated keyphrase embeddings into each phrase representation. A ranking network and VIB are then optimized together with rank loss and classification loss, respectively. This design of Diff-KPE allows us to rank each candidate phrase by utilizing both the information of keyphrases and the document. Experiments show that Diff-KPE outperforms existing KPE methods on a large open domain keyphrase extraction benchmark, OpenKP, and a scientific domain dataset, KP20K.

通过使用监督式的变分信息瓶颈，Diff-KPE能够利用关键短语信息来生成增强的关键短语表示，优化排序网络和分类网络，使得候选短语能够通过结合关键短语和文档信息进行排序。实验证明，Diff-KPE在大规模开放领域关键短语提取基准OpenKP和科学领域数据集KP20K上的性能优于现有的关键短语提取方法。

通过信息瓶颈引导的文本扩散过程增强短语表示用于关键词提取