SKGSum: Structured Knowledge-Guided Document Summarization

Qiqi Wang, Ruofan Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Jiamou Liu, Xianda Zheng, Zijian Huang

Abstract
A summary structure is inherent to certain types of texts according to the Genre Theory of Linguistics. Such structures aid readers in efficiently locating information within summaries. However, most existing automatic summarization methods overlook the importance of summary structure, resulting in summaries that emphasize the most prominent information while omitting essential details from other sections. While a few summarizers recognize the importance of summary structure, they rely heavily on the predefined labels of summary structures in the source document and ground truth summaries. To address these shortcomings, we developed a Structured Knowledge-Guided Summarization (SKGSum) and its variant, SKGSum-W, which do not require structure labels. Instead, these methods rely on a set of automatically extracted summary points to generate summaries. We evaluate the proposed methods using three real-world datasets. The results indicate that our methods not only improve the quality of summaries, in terms of ROUGE and BERTScore, but also broaden the types of documents that can be effectively summarized.
Anthology ID:
2024.findings-acl.110
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1857–1871
Language:
URL:
https://aclanthology.org/2024.findings-acl.110
DOI:
Bibkey:
Cite (ACL):
Qiqi Wang, Ruofan Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Jiamou Liu, Xianda Zheng, and Zijian Huang. 2024. SKGSum: Structured Knowledge-Guided Document Summarization. In Findings of the Association for Computational Linguistics ACL 2024, pages 1857–1871, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
SKGSum: Structured Knowledge-Guided Document Summarization (Wang et al., Findings 2024)
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