Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation

Han Guo, Ramakanth Pasunuru, Mohit Bansal


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-1064.pdf
Note:
 P18-1064.Notes.pdf
Data
CNN/Daily MailSNLISQuAD