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        <title>HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization</title>
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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.</abstract>
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%0 Conference Proceedings
%T HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
%A Zhang, Xingxing
%A Wei, Furu
%A Zhou, Ming
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-etal-2019-hibert
%X 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.
%R 10.18653/v1/P19-1499
%U https://aclanthology.org/P19-1499
%U https://doi.org/10.18653/v1/P19-1499
%P 5059-5069

Markdown (Informal)

[HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://aclanthology.org/P19-1499) (Zhang et al., ACL 2019)

• HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (Zhang et al., ACL 2019)

ACL

• Xingxing Zhang, Furu Wei, and Ming Zhou. 2019. HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5059–5069, Florence, Italy. Association for Computational Linguistics.