Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

Peng Cui, Le Hu, Yuanchao Liu


Abstract
Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents. They also often ignore the effect of topical information on capturing important contents. To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture inter-sentence relationships efficiently via graph-structured document representation. Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths. Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.
Anthology ID:
2020.coling-main.468
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5360–5371
Language:
URL:
https://aclanthology.org/2020.coling-main.468
DOI:
10.18653/v1/2020.coling-main.468
Bibkey:
Cite (ACL):
Peng Cui, Le Hu, and Yuanchao Liu. 2020. Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5360–5371, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (Cui et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.468.pdf