Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization

TL;DR

This paper introduces a Gist Detector that distills salient information from abstractive summarization models to improve long text understanding across various NLP tasks, achieving state-of-the-art results.

Contribution

It proposes a novel distillation-based Gist Detector to enhance long text understanding by focusing on relevant information, integrated as a supplementary component in existing models.

Findings

Significant improvement in document classification accuracy.

Enhanced performance in open-domain question answering.

State-of-the-art results in long text understanding tasks.

Abstract

Long text understanding is important yet challenging in natural language processing. A long article or essay usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. In this paper, we consider the problem of how to disentangle the gist-relevant and irrelevant information for long text understanding. With distillation mechanism, we transfer the knowledge about how to focus the salient parts from the abstractive summarization model and further integrate the distilled model, named \emph{Gist Detector}, into existing models as a supplementary component to augment the long text understanding. Experiments on document classification, distantly supervised open-domain question answering (DS-QA) and non-parallel text style transfer show that our method can significantly improve the performance of the baseline models, and achieves state-of-the-art overall results for document classification.