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Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT

2020-11-19 09:29:51
Ruifeng Yuan, Zili Wang, Wenjie Li
arXiv_CL
         
Abstract

Most current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that there exists a gap between the human-written gold summary and the oracle sentence labels. In this paper, we propose to extract fact-level semantic units for better extractive summarization. We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model. In addition, we incorporate our model with BERT using a hierarchical graph mask. This allows us to combine BERT's ability in natural language understanding and the structural information without increasing the scale of the model. Experiments on the CNN/DaliyMail dataset show that our model achieves state-of-the-art results.

Abstract (translated)

URL

https://arxiv.org/abs/2011.09739

PDF

https://arxiv.org/pdf/2011.09739.pdf

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