  BERT is a widely used pre-trained model in natural language processing.
However, since BERT is quadratic to the text length, the BERT model is
difficult to be used directly on the long-text corpus. In some fields, the
collected text data may be quite long, such as in the health care field.
Therefore, to apply the pre-trained language knowledge of BERT to long text, in
this paper, imitating the skimming-intensive reading method used by humans when
reading a long paragraph, the Skimming-Intensive Model (SkIn) is proposed. It
can dynamically select the critical information in the text so that the
sentence input into the BERT-Base model is significantly shortened, which can
effectively save the cost of the classification algorithm. Experiments show
that the SkIn method has achieved superior accuracy than the baselines on
long-text classification datasets in the medical field, while its time and
space requirements increase linearly with the text length, alleviating the time
and space overflow problem of basic BERT on long-text data.
