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arXiv · 1906.04466

Self-Supervised Learning for Contextualized Extractive Summarization

Hong Wang·Xin Wang·Wenhan Xiong·Mo Yu·Xiaoxiao Guo·Shiyu Chang·William Yang Wang
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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.

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Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang. 2019-06-11. Self-Supervised Learning for Contextualized Extractive Summarization. https://arxiv.org/abs/1906.04466

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