Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
Ruifeng Yuan, Zili Wang, Wenjie Li · 2020
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.