  Randomized controlled trials (RCTs) represent the paramount evidence of
clinical medicine. Using machines to interpret the massive amount of RCTs has
the potential of aiding clinical decision-making. We propose a RCT conclusion
generation task from the PubMed 200k RCT sentence classification dataset to
examine the effectiveness of sequence-to-sequence models on understanding RCTs.
We first build a pointer-generator baseline model for conclusion generation.
Then we fine-tune the state-of-the-art GPT-2 language model, which is
pre-trained with general domain data, for this new medical domain task. Both
automatic and human evaluation show that our GPT-2 fine-tuned models achieve
improved quality and correctness in the generated conclusions compared to the
baseline pointer-generator model. Further inspection points out the limitations
of this current approach and future directions to explore.
