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

ACL 2019

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

Feb 02, 2021
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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 inaccurate labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand for HIerachical Bidirectional Encoder Representations from Transformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained 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. Authors: Xingxing Zhang, Furu Wei, Ming Zhou (Microsoft Research)

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