HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
This addresses the problem of inaccurate labeling in document summarization for researchers and practitioners, representing a strong specific gain rather than a foundational advancement.
The paper tackled the challenge of training hierarchical encoders for neural extractive summarization with inaccurate sentence-level labels by proposing HIBERT, a document-level pre-training method using unlabeled data. The result was a 1.25 ROUGE improvement on CNN/Dailymail and 2.0 ROUGE on a New York Times dataset, achieving state-of-the-art performance.
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.