We present NewsQs (news-cues), a dataset that provides question-answer pairs
for multiple news documents. To create NewsQs, we augment a traditional
multi-document summarization dataset with questions automatically generated by
a T5-Large model fine-tuned on FAQ-style news articles from the News On the Web
corpus. We show that fine-tuning a model with control codes produces questions
that are judged acceptable more often than the same model without them as
measured through human evaluation. We use a QNLI model with high correlation
with human annotations to filter our data. We release our final dataset of
high-quality questions, answers, and document clusters as a resource for future
work in query-based multi-document summarization.

我们提供了一个名为 NewsQs (新闻提示) 的数据集，其中提供了多篇新闻文档的问题 - 回答对。通过在 News On the Web 语料库的 FAQ 样式新闻文章上对 T5-Large 模型进行微调，我们创造了 NewsQs，并自动生成了问题。我们证明，使用控制代码对模型进行微调可以生成更容易被人们接受的问题，与没有使用控制代码的相同模型相比，在人类评价中表现更好。我们使用与人类注释具有高相关性的 QNLI 模型来过滤数据。我们将我们的最终高质量问题、答案和文档聚类数据集作为资源，用于未来的基于查询的多文档摘要研究。