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    <abstract>As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github.</abstract>
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%0 Conference Proceedings
%T Heterogeneous Graph Neural Networks for Extractive Document Summarization
%A Wang, Danqing
%A Liu, Pengfei
%A Zheng, Yining
%A Qiu, Xipeng
%A Huang, Xuanjing
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-heterogeneous
%X As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github.
%R 10.18653/v1/2020.acl-main.553
%U https://aclanthology.org/2020.acl-main.553/
%U https://doi.org/10.18653/v1/2020.acl-main.553
%P 6209-6219

Markdown (Informal)

[Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://aclanthology.org/2020.acl-main.553/) (Wang et al., ACL 2020)

• Heterogeneous Graph Neural Networks for Extractive Document Summarization (Wang et al., ACL 2020)

ACL

• Danqing Wang, Pengfei Liu, Yining Zheng, Xipeng Qiu, and Xuanjing Huang. 2020. Heterogeneous Graph Neural Networks for Extractive Document Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6209–6219, Online. Association for Computational Linguistics.