  Despite increasing interest in the automatic detection of media frames in
NLP, the problem is typically simplified as single-label classification and
adopts a topic-like view on frames, evading modelling the broader
document-level narrative. In this work, we revisit a widely used
conceptualization of framing from the communication sciences which explicitly
captures elements of narratives, including conflict and its resolution, and
integrate it with the narrative framing of key entities in the story as heroes,
victims or villains. We adapt an effective annotation paradigm that breaks a
complex annotation task into a series of simpler binary questions, and present
an annotated data set of English news articles, and a case study on the framing
of climate change in articles from news outlets across the political spectrum.
Finally, we explore automatic multi-label prediction of our frames with
supervised and semi-supervised approaches, and present a novel retrieval-based
method which is both effective and transparent in its predictions. We conclude
with a discussion of opportunities and challenges for future work on
document-level models of narrative framing.
