  Structured (tabular) data in the preclinical and clinical domains contains
valuable information about individuals and an efficient table-to-text
summarization system can drastically reduce manual efforts to condense this
data into reports. However, in practice, the problem is heavily impeded by the
data paucity, data sparsity and inability of the state-of-the-art natural
language generation models (including T5, PEGASUS and GPT-Neo) to produce
accurate and reliable outputs. In this paper, we propose a novel table-to-text
approach and tackle these problems with a novel two-step architecture which is
enhanced by auto-correction, copy mechanism and synthetic data augmentation.
The study shows that the proposed approach selects salient biomedical entities
and values from structured data with improved precision (up to 0.13 absolute
increase) of copying the tabular values to generate coherent and accurate text
for assay validation reports and toxicology reports. Moreover, we also
demonstrate a light-weight adaptation of the proposed system to new datasets by
fine-tuning with as little as 40\% training examples. The outputs of our model
are validated by human experts in the Human-in-the-Loop scenario.
