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.

通过使用抽象化摘要生成模型的主旨检测能力，我们提出了 Gist Detector 来加强下游模型对长文本的理解能力，并评估了该方法在长文档分类、远程监督的开放领域问答以及非平行文本风格转换等三个不同任务上的性能表现，结果显示我们的方法在所有任务上都显著提升了基线模型的性能。