@InProceedings{zhang-EtAl:2018:LOUHI,
  author    = {Zhang, Yuhao  and  Ding, Daisy Yi  and  Qian, Tianpei  and  Manning, Christopher D.  and  Langlotz, Curtis P.},
  title     = {Learning to Summarize Radiology Findings},
  booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {204--213},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/W18-5623}
}