  News articles are driven by the informational sources journalists use in
reporting. Modeling when, how and why sources get used together in stories can
help us better understand the information we consume and even help journalists
with the task of producing it. In this work, we take steps toward this goal by
constructing the largest and widest-ranging annotated dataset, to date, of
informational sources used in news writing. We show that our dataset can be
used to train high-performing models for information detection and source
attribution. We further introduce a novel task, source prediction, to study the
compositionality of sources in news articles. We show good performance on this
task, which we argue is an important proof for narrative science exploring the
internal structure of news articles and aiding in planning-based language
generation, and an important step towards a source-recommendation system to aid
journalists.
