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。

NewsQs: 多源信息提问

NewsQs: Multi-Source Question Generation for the Inquiring Mind

Contextual commonsense inference is the task of generating various types of
explanations around the events in a dyadic dialogue, including cause,
motivation, emotional reaction, and others. Producing a coherent and
non-trivial explanation requires awareness of the dialogue's structure and of
how an event is grounded in the context. In this work, we create CICEROv2, a
dataset consisting of 8,351 instances from 2,379 dialogues, containing multiple
human-written answers for each contextual commonsense inference question,
representing a type of explanation on cause, subsequent event, motivation, and
emotional reaction. We show that the inferences in CICEROv2 are more
semantically diverse than other contextual commonsense inference datasets. To
solve the inference task, we propose a collection of pre-training objectives,
including concept denoising and utterance sorting to prepare a pre-trained
model for the downstream contextual commonsense inference task. Our results
show that the proposed pre-training objectives are effective at adapting the
pre-trained T5-Large model for the contextual commonsense inference task.

该论文介绍了一个包含多种类型的文本解释的数据集 CICEROv2，提出了一系列的预训练目标来帮助下游任务。结果表明，该论文中的预训练目标对于预训练模型来说是有效的。