Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers

EMNLP, pp. 1784-1795, 2020.

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Abstract:

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sente...More

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