Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly. In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases. In UniKeyphrase, stacked relation layer and bag-of-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively. Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin.

中文翻译：

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UniKeyphrase：关键短语预测的统一提取和生成框架

Keyphrase Prediction (KP) 任务旨在预测可以总结给定文档主要思想的几个关键短语。主流的 KP 方法可以分为纯生成方法和具有提取和生成的集成模型。然而，这些方法要么忽略了关键短语之间的多样性，要么只能隐式地弱捕捉任务之间的关系。在本文中，我们提出了 UniKeyphrase，这是一种新颖的端到端学习框架，可共同学习提取和生成关键短语。在 UniKeyphrase 中，分别从模型结构和训练过程的角度，提出了堆叠关系层和词袋约束，以充分利用提取和生成之间的潜在语义关系。KP 基准的实验表明，我们的联合方法大大优于主流方法。