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    <abstract>Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level. In this paper, we aim to improve this task by introducing three auxiliary pre-training tasks that learn to capture the document-level context in a self-supervised fashion. Experiments on the widely-used CNN/DM dataset validate the effectiveness of the proposed auxiliary tasks. Furthermore, we show that after pre-training, a clean model with simple building blocks is able to outperform previous state-of-the-art that are carefully designed.</abstract>
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%0 Conference Proceedings
%T Self-Supervised Learning for Contextualized Extractive Summarization
%A Wang, Hong
%A Wang, Xin
%A Xiong, Wenhan
%A Yu, Mo
%A Guo, Xiaoxiao
%A Chang, Shiyu
%A Wang, William Yang
%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 wang-etal-2019-self-supervised
%X Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level. In this paper, we aim to improve this task by introducing three auxiliary pre-training tasks that learn to capture the document-level context in a self-supervised fashion. Experiments on the widely-used CNN/DM dataset validate the effectiveness of the proposed auxiliary tasks. Furthermore, we show that after pre-training, a clean model with simple building blocks is able to outperform previous state-of-the-art that are carefully designed.
%R 10.18653/v1/P19-1214
%U https://aclanthology.org/P19-1214
%U https://doi.org/10.18653/v1/P19-1214
%P 2221-2227

Markdown (Informal)

[Self-Supervised Learning for Contextualized Extractive Summarization](https://aclanthology.org/P19-1214) (Wang et al., ACL 2019)

• Self-Supervised Learning for Contextualized Extractive Summarization (Wang et al., ACL 2019)

ACL

• Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, and William Yang Wang. 2019. Self-Supervised Learning for Contextualized Extractive Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2221–2227, Florence, Italy. Association for Computational Linguistics.