论文标题
用于提取文档摘要的异质图神经网络
Heterogeneous Graph Neural Networks for Extractive Document Summarization
论文作者
论文摘要
作为提取文档摘要的关键步骤，多种方法已经探索了学习跨句子关系。直观的方法是将它们放入基于图的神经网络中，该网络具有更复杂的捕获句子间关系的结构。在本文中，我们提出了一个基于图形的神经网络，用于提取性摘要（hetersumgraph），该网络包含不同粒度水平的语义节点，除了句子。这些额外的节点充当句子之间的中介，并丰富跨句子关系。此外，通过引入文档节点，我们的图形结构在从单文件设置到多文档的自然扩展方面具有灵活性。据我们所知，我们是第一个将不同类型的节点引入基于图的神经网络的人，以提取文档摘要并进行全面的定性分析以调查其益处。该代码将在github上发布
As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HeterSumGraph), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github
