  The rapid growth in literature accumulates diverse and yet comprehensive
biomedical knowledge hidden to be mined such as drug interactions. However, it
is difficult to extract the heterogeneous knowledge to retrieve or even
discover the latest and novel knowledge in an efficient manner. To address such
a problem, we propose EGFI for extracting and consolidating drug interactions
from large-scale medical literature text data. Specifically, EGFI consists of
two parts: classification and generation. In the classification part, EGFI
encompasses the language model BioBERT which has been comprehensively
pre-trained on biomedical corpus. In particular, we propose the multi-head
attention mechanism and pack BiGRU to fuse multiple semantic information for
rigorous context modeling. In the generation part, EGFI utilizes another
pre-trained language model BioGPT-2 where the generation sentences are selected
based on filtering rules. We evaluated the classification part on "DDIs 2013"
dataset and "DTIs" dataset, achieving the FI score of 0.842 and 0.720
respectively. Moreover, we applied the classification part to distinguish
high-quality generated sentences and verified with the exiting growth truth to
confirm the filtered sentences. The generated sentences that are not recorded
in DrugBank and DDIs 2013 dataset also demonstrate the potential of EGFI to
identify novel drug relationships.
