论文标题
由潜在层次文档结构指导的抽象性摘要
Abstractive Summarization Guided by Latent Hierarchical Document Structure
论文作者
论文摘要
顺序的抽象神经摘要通常不使用输入文章中的基本结构或输入句子之间的依赖性。这种结构对于整合和合并文本不同部分的信息至关重要。为了解决这一缺点，我们提出了一个层次结构感知的图形神经网络（HIERGNN），该图通过三个主要步骤捕获此类依赖项：1）通过通过稀疏的矩阵树计算学到的潜在结构树学习层次结构的结构； 2）使用新的消息传播节点传播机制在此结构上传播句子信息，以识别显着信息； 3）使用图形级别的注意力将解码器集中在显着信息上。实验证实HIERGNN改善了强大的序列模型，例如BART，对于CNN/DM和XSUM，平均胭脂1/2/L的0.55和0.75余量。进一步的人类评估表明，与基准相比，我们模型产生的摘要更相关，更冗余，并纳入了Hiergnn。我们还发现Hiergnn通过更多地融合多个源句子，而不是压缩单个源句子，从而综合了摘要，并且它可以更有效地处理长时间的输入。
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to integrate and consolidate information from different parts of the text. To address this shortcoming, we propose a hierarchy-aware graph neural network (HierGNN) which captures such dependencies through three main steps: 1) learning a hierarchical document structure through a latent structure tree learned by a sparse matrix-tree computation; 2) propagating sentence information over this structure using a novel message-passing node propagation mechanism to identify salient information; 3) using graph-level attention to concentrate the decoder on salient information. Experiments confirm HierGNN improves strong sequence models such as BART, with a 0.55 and 0.75 margin in average ROUGE-1/2/L for CNN/DM and XSum. Further human evaluation demonstrates that summaries produced by our model are more relevant and less redundant than the baselines, into which HierGNN is incorporated. We also find HierGNN synthesizes summaries by fusing multiple source sentences more, rather than compressing a single source sentence, and that it processes long inputs more effectively.
