Publication
ACL 2019
Conference paper

Self-supervised learning for contextualized extractive summarization

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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 pretraining, a clean model with simple building blocks is able to outperform previous state-of-the-art that are carefully designed.

Date

28 Jul 2019

Publication

ACL 2019

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