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.

翻译：大多数现有的多文档摘要（MDS）数据集缺乏人工生成的、真实的（即非合成的）摘要，或带有摘要必须捕捉的显式文档间关系的源文档。为增强MDS系统的能力，我们提出PeerSum，一个用于生成科学论文元评论的新型数据集，其中的元评论是对评论及相应讨论的高度抽象且真实的摘要。这些源文档具有丰富的文档间关系，呈现显式的层次结构，包含交叉引用，且常存在冲突。鉴于通过预训练语言模型上的注意力机制将层次关系融入MDS系统的研究尚显不足，我们还提出了Rammer（关系感知的多任务元评论生成器），该模型基于层次关系使用稀疏注意力，并采用多任务目标，除标准文本生成目标外还预测若干元数据特征。实验结果表明，PeerSum是一个具有挑战性的数据集，且Rammer在多种评估指标下优于其他强基线MDS模型。