Generating concise summaries of news events is a challenging natural language
processing task. While journalists often curate timelines to highlight key
sub-events, newcomers to a news event face challenges in catching up on its
historical context. In this paper, we address this need by introducing the task
of background news summarization, which complements each timeline update with a
background summary of relevant preceding events. We construct a dataset by
merging existing timeline datasets and asking human annotators to write a
background summary for each timestep of each news event. We establish strong
baseline performance using state-of-the-art summarization systems and propose a
query-focused variant to generate background summaries. To evaluate background
summary quality, we present a question-answering-based evaluation metric,
Background Utility Score (BUS), which measures the percentage of questions
about a current event timestep that a background summary answers. Our
experiments show the effectiveness of instruction fine-tuned systems such as
Flan-T5, in addition to strong zero-shot performance using GPT-3.5.

通过引入背景新闻摘要机制，结合时间轴更新，我们构建了一个数据集，并通过人工标注人员为每个新闻事件的每个时间步骤编写了背景摘要。我们提出了一个问题导向的变体来生成背景摘要，并使用基于问答的评估指标 Background Utility Score（BUS）评估了摘要质量。实验结果表明，在 Fine-tuned 系统（如 Flan-T5）和零 - shot 性能强大的 GPT-3.5 的指导下，背景摘要表现出很好的效果。