Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT

• URL: http://arxiv.org/abs/2011.09739v1
• Date: Thu, 19 Nov 2020 09:29:51 GMT
• Title: Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
• Authors: Ruifeng Yuan, Zili Wang, Wenjie Li
• Abstract summary: We propose to extract fact-level semantic units for better extractive summarization. We incorporate our model with BERT using a hierarchical graph mask. Experiments on the CNN/DaliyMail dataset show that our model achieves state-of-the-art results.
• Score: 9.271716501646194
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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