  Extracting appropriate features to represent a corpus is an important task
for textual mining. Previous attention based work usually enhance feature at
the lexical level, which lacks the exploration of feature augmentation at the
sentence level. In this paper, we exploit a Dynamic Feature Generation Network
(DFGN) to solve this problem. Specifically, DFGN generates features based on a
variety of attention mechanisms and attaches features to sentence
representation. Then a thresholder is designed to filter the mined features
automatically. DFGN extracts the most significant characteristics from datasets
to keep its practicability and robustness. Experimental results on multiple
well-known answer selection datasets show that our proposed approach
significantly outperforms state-of-the-art baselines. We give a detailed
analysis of the experiments to illustrate why DFGN provides excellent retrieval
and interpretative ability.
