Embedding based methods are widely used for unsupervised keyphrase extraction
(UKE) tasks. Generally, these methods simply calculate similarities between
phrase embeddings and document embedding, which is insufficient to capture
different context for a more effective UKE model. In this paper, we propose a
novel method for UKE, where local and global contexts are jointly modeled. From
a global view, we calculate the similarity between a certain phrase and the
whole document in the vector space as transitional embedding based models do.
In terms of the local view, we first build a graph structure based on the
document where phrases are regarded as vertices and the edges are similarities
between vertices. Then, we proposed a new centrality computation method to
capture local salient information based on the graph structure. Finally, we
further combine the modeling of global and local context for ranking. We
evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010)
and compare with existing state-of-the-art models. The results show that our
model outperforms most models while generalizing better on input documents with
different domains and length. Additional ablation study shows that both the
local and global information is crucial for unsupervised keyphrase extraction
tasks.

本文提出了一种新的方法来解决无监督关键词抽取任务，该方法联合建模本地和全局语境。本地语境是通过基于文档的图形结构计算的，而全局语境是通过矢量空间中的转型嵌入模型计算的。最后，联合建模全局和本地语境以进行排名。

联合建模本地和全局语境的无监督关键词提取

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global  Context

We study the problem of generating keyphrases that summarize the key points
for a given document. While sequence-to-sequence (seq2seq) models have achieved
remarkable performance on this task (Meng et al., 2017), model training often
relies on large amounts of labeled data, which is only applicable to
resource-rich domains. In this paper, we propose semi-supervised keyphrase
generation methods by leveraging both labeled data and large-scale unlabeled
samples for learning. Two strategies are proposed. First, unlabeled documents
are first tagged with synthetic keyphrases obtained from unsupervised keyphrase
extraction methods or a selflearning algorithm, and then combined with labeled
samples for training. Furthermore, we investigate a multi-task learning
framework to jointly learn to generate keyphrases as well as the titles of the
articles. Experimental results show that our semi-supervised learning-based
methods outperform a state-of-the-art model trained with labeled data only.

本篇论文探讨了如何利用半监督学习方法，在有限的有标签数据下，将无标签数据与有标签数据结合来提高关键短语生成的性能，实现多任务学习，通过无监督关键短语抽取方法或自学习算法得到的合成关键短语为无标签数据进行标注。实验结果表明，利用半监督学习的方法能够明显优于仅训练有标签数据的最先进模型。