MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization
Qianren Mao, Hongdong Zhu, Junnan Liu, Cheng Ji, Hao Peng, Jianxin Li, Lihong Wang, Zheng Wang · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Recent studies of extractive text summarization have leveraged BERT for document encoding with breakthrough performance. However, when using a pre-trained BERT-based encoder, existing approaches for selecting representative sentences for text summarization are inadequate since the encoder is not explicitly trained for representing sentences. Simply providing the BERT-initialized sentences to cross-sentential graph-based neural networks (GNNs) to encode semantic features of the sentences is not ideal because doing so fail to integrate other summary-worthy features like sentence importance and positions. This paper presents MuchSUM, a better approach for extractive text summarization. MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features. Specifically, we introduce three specific graph channels to encode the node textual features, node centrality features, and node position features, respectively, under bipartite word-sentence heterogeneous graphs. Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels. Finally, the sentence representations of each channel are fused for extractive summarization. We also investigate three weighted graphs in each channel to infuse edge features for graph-based summarization modeling. Experimental results demonstrate our model can achieve considerable performance compared with some BERT-initialized graph-based extractive summarization systems.