Background
In binary classification problem of dataset consisting of sequences of events, performance varies across types of models:

for sequence of events, LSTM gives an average precision score of APS = 0.60

for features engineered with summary stats (first-last occurrence, overall frequency etc), LightGBM gives an APS = 0.80


Dataset specs

The dataset is highly imbalance (ratio=0.1).
Number of unique tokens is <100


Questions

Would there be any obvious reason for the above that I am missing?
Importantly, what would be a way to combine the LSTM and lightGBM ports?