论文标题
通过句法图形卷积网络的选择性注意编码器用于文档摘要
Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
论文作者
论文摘要
抽象文本摘要是一项具有挑战性的任务，需要设计一种机制来有效地从源文本中提取显着信息，然后产生摘要。源文本的解析过程包含关键的句法或语义结构，这对于生成更准确的摘要非常有用。但是，由于其非线性结构而建模以进行文本摘要的解析树并不是一件容易的事，并且很难处理包含多个句子及其解析树的文档。在本文中，我们建议使用图形将解析树连接到文档中的句子，并利用堆叠的图形卷积网络（GCN）学习文档的语法表示。选择性注意机制用于在语义和结构方面提取显着信息，并产生抽象性摘要。我们在CNN/每日邮件文本摘要数据集上评估我们的方法。实验结果表明，提出的基于GCN的选择性注意方法优于基准，并在数据集中实现最新性能。
Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or semantic structures, which is useful to generate more accurate summary. However, modeling a parsing tree for text summarization is not trivial due to its non-linear structure and it is harder to deal with a document that includes multiple sentences and their parsing trees. In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspect and generate an abstractive summary. We evaluate our approach on the CNN/Daily Mail text summarization dataset. The experimental results show that the proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.
