  With the rapid development of precision medicine, a large amount of health
data (such as electronic health records, gene sequencing, medical images, etc.)
has been produced. It encourages more and more interest in data-driven insight
discovery from these data. It is a reasonable way to verify the derived
insights in biomedical literature. However, manual verification is inefficient
and not scalable. Therefore, an intelligent technique is necessary to solve
this problem. In this paper, we propose a task of biomedical evidence
generation, which is very novel and different from existing NLP tasks.
Furthermore, we developed a biomedical evidence generation engine for this task
with the pipeline of three components which are a literature retrieval module,
a skeleton information identification module, and a text summarization module.
