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Self-Supervised Learning for Contextualized Extractive Summarization
[article]
2019
arXiv
pre-print
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
arXiv:1906.04466v1
fatcat:qiwejln6vvetzpvsucejtyvzbi