Writers such as journalists often use automatic tools to find relevant content to include in their narratives. In this paper, we focus on supporting writers in the news domain to develop event-centric narratives. Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative. We formally define this task and propose a retrieval dataset construction procedure that relies on existing news articles to simulate incomplete narratives and relevant articles. Experiments on two datasets derived from this procedure show that state-of-the-art lexical and semantic rankers are not sufficient for this task. We show that combining those with a ranker that ranks articles by reverse chronological order outperforms those rankers alone. We also perform an in-depth quantitative and qualitative analysis of the results that sheds light on the characteristics of this task.

中文翻译：

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新闻文章在以事件为中心的叙事创作的上下文中检索

记者等作家经常使用自动工具来查找相关内容以包含在他们的叙述中。在本文中，我们专注于支持新闻领域的作家开发以事件为中心的叙事。鉴于指定主要事件和上下文的不完整叙述，我们的目标是检索讨论相关事件的新闻文章，从而使叙述得以延续。我们正式定义了这项任务，并提出了一个检索数据集构建程序，该程序依赖于现有的新闻文章来模拟不完整的叙述和相关文章。在此过程中衍生的两个数据集上进行的实验表明，最先进的词汇和语义排名器不足以完成此任务。我们表明，将这些与按时间倒序对文章进行排名的排名器相结合，效果优于单独使用这些排名器。