Abstract
An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model’s learned saliency and entailment skills.- Anthology ID:
- P18-1064
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 687–697
- Language:
- URL:
- https://aclanthology.org/P18-1064
- DOI:
- 10.18653/v1/P18-1064
- Cite (ACL):
- Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018. Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 687–697, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (Guo et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/P18-1064.pdf
- Data
- CNN/Daily Mail, SNLI, SQuAD