Pre-trained language models (PLMs) have accomplished impressive achievements in abstractive single-document summarization (SDS). However, such benefits may not be readily extended to muti-document summarization (MDS), where the interactions among documents are more complex. Previous works either design new architectures or new pre-training objectives for MDS, or apply PLMs to MDS without considering the complex document interactions. While the former does not make full use of previous pre-training efforts and may not generalize well across multiple domains, the latter cannot fully attend to the intricate relationships unique to MDS tasks. In this paper, we enforce hierarchy on both the encoder and decoder and seek to make better use of a PLM to facilitate multi-document interactions for the MDS task. We test our design on 10 MDS datasets across a wide range of domains. Extensive experiments show that our proposed method can achieve consistent improvements on all these datasets, outperforming the previous best models, and even achieving better or competitive results as compared to some models with additional MDS pre-training or larger model parameters.

翻译：预训练语言模型（PLMs）已在抽象式单文档摘要（SDS）中取得了显著成就。然而，这些优势可能难以直接扩展到多文档摘要（MDS）任务，因为文档间的交互更为复杂。先前的研究要么为MDS设计新架构或新预训练目标，要么将PLMs直接应用于MDS而不考虑复杂的文档交互。前者未能充分利用以往的预训练成果，且跨域泛化能力有限；后者则无法充分处理MDS任务特有的复杂关系。本文在编码器和解码器上均引入分层结构，旨在更有效地利用PLM促进MDS任务中的多文档交互。我们在涵盖多个领域的10个MDS数据集上测试了设计方案。大量实验表明，所提方法能在所有数据集上取得一致改进，超越先前最优模型，甚至与部分采用额外MDS预训练或更大模型参数的模型相比，也能达到更优或具有竞争力的结果。