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.

本研究提供了一种新的方法，通过采用编码器和解码器的分层方法来更好地利用预训练语言模型，以促进多文档任务的交互。大量实验表明，该方法可以在各种领域的 10 个多文档数据集上实现一致的提高，优于以前的最佳模型，并且甚至与某些具有额外多文档预训练或更大模型参数的模型相比具有更好的或竞争性的结果。