  Framing a news article means to portray the reported event from a specific
perspective, e.g., from an economic or a health perspective. Reframing means to
change this perspective. Depending on the audience or the submessage, reframing
can become necessary to achieve the desired effect on the readers. Reframing is
related to adapting style and sentiment, which can be tackled with neural text
generation techniques. However, it is more challenging since changing a frame
requires rewriting entire sentences rather than single phrases. In this paper,
we study how to computationally reframe sentences in news articles while
maintaining their coherence to the context. We treat reframing as a
sentence-level fill-in-the-blank task for which we train neural models on an
existing media frame corpus. To guide the training, we propose three
strategies: framed-language pretraining, named-entity preservation, and
adversarial learning. We evaluate respective models automatically and manually
for topic consistency, coherence, and successful reframing. Our results
indicate that generating properly-framed text works well but with tradeoffs.
