We aim to renew interest in a particular multi-document summarization (MDS)
task which we call AgreeSum: agreement-oriented multi-document summarization.
Given a cluster of articles, the goal is to provide abstractive summaries that
represent information common and faithful to all input articles. Given the lack
of existing datasets, we create a dataset for AgreeSum, and provide annotations
on article-summary entailment relations for a subset of the clusters in the
dataset. We aim to create strong baselines for the task by applying the
top-performing pretrained single-document summarization model PEGASUS onto
AgreeSum, leveraging both annotated clusters by supervised losses, and
unannotated clusters by T5-based entailment-related and language-related
losses. Compared to other baselines, both automatic evaluation and human
evaluation show better article-summary and cluster-summary entailment in
generated summaries. On a separate note, we hope that our article-summary
entailment annotations contribute to the community's effort in improving
abstractive summarization faithfulness.

我们在建立包含多篇文章的数据集基础上，对 MDS 任务的 AgreeSum 分支进行了研究，通过应用 PEGASUS 模型以及有监督的损失函数和 T5 模型的损失函数进行分析，实现了更好的摘要及其准确度。