  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.
