  Long text understanding is important yet challenging for natural language
processing. A long article or document usually contains many redundant words
that are not pertinent to its gist and sometimes can be regarded as noise. With
recent advances of abstractive summarization, we propose our \emph{Gist
Detector} to leverage the gist detection ability of a summarization model and
integrate the extracted gist into downstream models to enhance their long text
understanding ability. Specifically, Gist Detector first learns the gist
detection knowledge distilled from a summarization model, and then produces
gist-aware representations to augment downstream models. We evaluate our method
on three different tasks: long document classification, distantly supervised
open-domain question answering, and non-parallel text style transfer. The
experimental results show that our method can significantly improve the
performance of baseline models on all tasks.
