Improving Long Text Understanding with Knowledge Distilled from Summarization Model
This addresses the challenge of noise in long texts for NLP applications, though it is incremental as it builds on existing summarization techniques.
The authors tackled long text understanding by distilling gist detection knowledge from a summarization model into a Gist Detector, which improved baseline model performance on tasks like long document classification and question answering.
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.