Leveraging Graph to Improve Abstractive Multi-Document Summarization

Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, Junping Du


Abstract
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.
Anthology ID:
2020.acl-main.555
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6232–6243
Language:
URL:
https://aclanthology.org/2020.acl-main.555
DOI:
10.18653/v1/2020.acl-main.555
Bibkey:
Cite (ACL):
Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, and Junping Du. 2020. Leveraging Graph to Improve Abstractive Multi-Document Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6232–6243, Online. Association for Computational Linguistics.
Cite (Informal):
Leveraging Graph to Improve Abstractive Multi-Document Summarization (Li et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.555.pdf
Video:
 http://slideslive.com/38929010
Code
 PaddlePaddle/Research
Data
WikiSum