We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures. We address several aspects of summarization that are not well modeled by the basic architecture, by integrating multiple layers of the encoder, controlling information flow in the hierarchy, and exploiting external knowledge. First, we propose a simple and efficient deep layer fusion to extract salient information from the encoder layers. Second, we propose a gating mechanism to control and maintain important contextual information through the encoder-decoder layers into dynamic convolutions. Lastly, we put part-of-speech information into the model as external knowledge to better predict filters for dynamic convolutions. We evaluate our model using ROUGE metrics on three different datasets: CNN-DM, NEWSROOM-ABS, and XSUM. Experimental results show that the proposed model outperforms the state-of-the-art abstractive models on NEWSROOM-ABS and XSUM and shows comparable scores on CNN-DM.

我们提出了一种基于最近提出的动态卷积编码器-解码器体系结构的新颖的抽象文档摘要。通过集成编码器的多个层，控制层次结构中的信息流以及利用外部知识，我们解决了基本架构无法很好地建模的几个方面。首先，我们提出了一种简单有效的深层融合方法，以从编码器层中提取显着信息。其次，我们提出了一种门控机制，通过编码器/解码器层将重要的上下文信息控制和维护为动态卷积。最后，我们将词性信息作为外部知识放入模型中，以更好地预测动态卷积的滤波器。我们在三个不同的数据集上使用ROUGE指标评估了我们的模型：CNN-DM，NEWSROOM-ABS，和XSUM。实验结果表明，所提出的模型优于NEWSROOM-ABS和XSUM上的最新抽象模型，并且在CNN-DM上显示出可比的分数。