Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.

介绍了一种新的标题引导网络(TG-Net)模型，该模型通过编码器-解码器架构和两个新特性：（i）标题作为类似查询的输入，（ii）标题引导编码器将有关信息从标题收集到文档的每个单词中，以解决自动关键短语生成任务中标题对整个文档的引领作用被忽略的问题。该模型在一系列KG数据集上的实验表明，其性能优于现有的各种模型，特别是对于标题长度比率非常低或非常高的文档。

基于标题引导的关键词生成编码