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        <title>Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT</title>
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    <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>
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%0 Conference Proceedings
%T Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
%A Yuan, Ruifeng
%A Wang, Zili
%A Li, Wenjie
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F yuan-etal-2020-fact
%X 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.
%R 10.18653/v1/2020.coling-main.493
%U https://aclanthology.org/2020.coling-main.493
%U https://doi.org/10.18653/v1/2020.coling-main.493
%P 5629-5639

Markdown (Informal)

[Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT](https://aclanthology.org/2020.coling-main.493) (Yuan et al., COLING 2020)

• Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT (Yuan et al., COLING 2020)

ACL

• Ruifeng Yuan, Zili Wang, and Wenjie Li. 2020. Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5629–5639, Barcelona, Spain (Online). International Committee on Computational Linguistics.