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        <title>Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
%A Guo, Han
%A Pasunuru, Ramakanth
%A Bansal, Mohit
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F guo-etal-2018-soft
%X 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.
%R 10.18653/v1/P18-1064
%U https://aclanthology.org/P18-1064
%U https://doi.org/10.18653/v1/P18-1064
%P 687-697

Markdown (Informal)

[Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation](https://aclanthology.org/P18-1064) (Guo et al., ACL 2018)

• Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (Guo et al., ACL 2018)

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.