HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization

TL;DR

HIBERT introduces a hierarchical transformer-based pre-training method for document encoding, significantly improving extractive summarization performance on benchmark datasets by leveraging unlabeled data.

Contribution

The paper proposes HIBERT, a novel hierarchical transformer pre-training approach for document encoding that enhances extractive summarization accuracy.

Findings

Outperforms baseline models with 1.25 ROUGE improvement on CNN/DailyMail

Achieves 2.0 ROUGE points better on New York Times dataset

Sets new state-of-the-art results on both datasets

Abstract

Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \emph{inaccurate} labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders \cite{devlin:2018:arxiv}, we propose {\sc Hibert} (as shorthand for {\bf HI}erachical {\bf B}idirectional {\bf E}ncoder {\bf R}epresentations from {\bf T}ransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained {\sc Hibert} to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.