基于句子选择的关键短语生成

关键短语生成是一个能从长文档或者文献中捕获中心思想的实用任务.先前的神经关键短语生成方法基本只注重词级别的信息而忽略文档结构.该文提出了一个句级选择网络(sentence selective network,SenSeNet)用于关键短语生成.该模型重点关注文档的句子结构信息,通过学习句子隐式表示来判断其是否有可能生成...

Neural Keyphrase Generation with Sentence Selective Network

Keyphrase Generation (KG) is the task of capturing themes from a document, revealing the key information necessary to understand the content. Existing neural keyphrase generation approaches focus only on the token-level information while ignore sentence-level information such as document structure. In this paper, we incorporate the sentence-level inductive bias into KG and propose a new method named Sentence Selective Network (SenSeNet), which can automatically learn the sentence-level information and determine whether the sentence more likely to generate the keyphrase. We use straight-through estimator to train the model in an end-to-end manner and incorporate a weakly-supervised setting which is helpful for the training of the sentence selection module. Experiments show that our model successfully captures the document structure and reasonably distinguishes the significance of sentences, and consistent improvements achieved on two metrics in five datasets.