A critical point of multi-document summarization (MDS) is to learn the
relations among various documents. In this paper, we propose a novel
abstractive MDS model, in which we represent multiple documents as a
heterogeneous graph, taking semantic nodes of different granularities into
account, and then apply a graph-to-sequence framework to generate summaries.
Moreover, we employ a neural topic model to jointly discover latent topics that
can act as cross-document semantic units to bridge different documents and
provide global information to guide the summary generation. Since topic
extraction can be viewed as a special type of summarization that "summarizes"
texts into a more abstract format, i.e., a topic distribution, we adopt a
multi-task learning strategy to jointly train the topic and summarization
module, allowing the promotion of each other. Experimental results on the
Multi-News dataset demonstrate that our model outperforms previous
state-of-the-art MDS models on both Rouge metrics and human evaluation,
meanwhile learns high-quality topics.

该论文提出了一种基于异构图和神经主题模型的抽象多文档摘要模型，采用图到序列的框架，同时训练主题和摘要模块以提高性能和生成高质量主题。