@inproceedings{wang-etal-2024-skgsum,
title = "{SKGS}um: Structured Knowledge-Guided Document Summarization",
author = "Wang, Qiqi and
Wang, Ruofan and
Zhao, Kaiqi and
Amor, Robert and
Liu, Benjamin and
Liu, Jiamou and
Zheng, Xianda and
Huang, Zijian",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.110/",
doi = "10.18653/v1/2024.findings-acl.110",
pages = "1857--1871",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T SKGSum: Structured Knowledge-Guided Document Summarization
%A Wang, Qiqi
%A Wang, Ruofan
%A Zhao, Kaiqi
%A Amor, Robert
%A Liu, Benjamin
%A Liu, Jiamou
%A Zheng, Xianda
%A Huang, Zijian
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-skgsum
%X 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.
%R 10.18653/v1/2024.findings-acl.110
%U https://aclanthology.org/2024.findings-acl.110/
%U https://doi.org/10.18653/v1/2024.findings-acl.110
%P 1857-1871
Markdown (Informal)
[SKGSum: Structured Knowledge-Guided Document Summarization](https://aclanthology.org/2024.findings-acl.110/) (Wang et al., Findings 2024)
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. Association for Computational Linguistics.