Most existing multi-document summarization (MDS) datasets lack
human-generated and genuine (i.e., not synthetic) summaries or source documents
with explicit inter-document relationships that a summary must capture. To
enhance the capabilities of MDS systems we present PeerSum, a novel dataset for
generating meta-reviews of scientific papers, where the meta-reviews are highly
abstractive and genuine summaries of reviews and corresponding discussions.
These source documents have rich inter-document relationships of an explicit
hierarchical structure with cross-references and often feature conflicts. As
there is a scarcity of research that incorporates hierarchical relationships
into MDS systems through attention manipulation on pre-trained language models,
we additionally present Rammer (Relationship-aware Multi-task Meta-review
Generator), a meta-review generation model that uses sparse attention based on
the hierarchical relationships and a multi-task objective that predicts several
metadata features in addition to the standard text generation objective. Our
experimental results show that PeerSum is a challenging dataset, and Rammer
outperforms other strong baseline MDS models under various evaluation metrics.

本文介绍 PeerSum 数据集以生成高度抽象的科学论文审稿的 meta-reviews，并介绍了 Rammer 模型来通过稀疏注意力机制预测元数据特征，从而提高多文档摘要系统的能力。