  The Impression section of a radiology report summarizes crucial radiology
findings in natural language and plays a central role in communicating these
findings to physicians. However, the process of generating impressions by
summarizing findings is time-consuming for radiologists and prone to errors. We
propose to automate the generation of radiology impressions with neural
sequence-to-sequence learning. We further propose a customized neural model for
this task which learns to encode the study background information and use this
information to guide the decoding process. On a large dataset of radiology
reports collected from actual hospital studies, our model outperforms existing
non-neural and neural baselines under the ROUGE metrics. In a blind experiment,
a board-certified radiologist indicated that 67% of sampled system summaries
are at least as good as the corresponding human-written summaries, suggesting
significant clinical validity. To our knowledge our work represents the first
attempt in this direction.
