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Self-Supervised Learning for Contextualized Extractive Summarization

2019-09-23 | |  98 |   46 |   0 0 0
Abstract Existing models for extractive summarization are usually trained from scratch with a crossentropy 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 pretraining, a clean model with simple building blocks is able to outperform previous state-ofthe-art that are carefully designed

上一篇：Searching for Effective Neural Extractive Summarization: What Works and What’s Next

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