  A chest X-ray radiology report describes abnormal findings not only from
X-ray obtained at current examination, but also findings on disease progression
or change in device placement with reference to the X-ray from previous
examination. Majority of the efforts on automatic generation of radiology
report pertain to reporting the former, but not the latter, type of findings.
To the best of the authors' knowledge, there is only one work dedicated to
generating summary of the latter findings, i.e., follow-up summary. In this
study, we therefore propose a transformer-based framework to tackle this task.
Motivated by our observations on the significance of medical lexicon on the
fidelity of summary generation, we introduce two mechanisms to bestow expert
insight to our model, namely expert soft guidance and masked entity modeling
loss. The former mechanism employs a pretrained expert disease classifier to
guide the presence level of specific abnormalities, while the latter directs
the model's attention toward medical lexicon. Extensive experiments were
conducted to demonstrate that the performance of our model is competitive with
or exceeds the state-of-the-art.
