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.- Anthology ID:
- P19-1499
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5059–5069
- Language:
- URL:
- https://aclanthology.org/P19-1499
- DOI:
- 10.18653/v1/P19-1499
- Cite (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.
- Cite (Informal):
- HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (Zhang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-1499.pdf
- Data
- CNN/Daily Mail