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Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge

文献类型：期刊论文

作者Mao, Qianren6,7; Li, Jianxin6,7; Peng, Hao6,7; He, Shizhu4,5; Wang, Lihong3; Yu, Philip S.2; Wang, Zheng1
刊名IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
出版日期2022
卷号30页码:1665-1678
关键词Fact consistency graph neural network language model pointer network text summarization
ISSN号2329-9290
DOI10.1109/TASLP.2022.3161157
通讯作者Li, Jianxin(lijx@act.buaa.edu.cn)
英文摘要. Abstractive summarization generates a concise summary to capture the key ideas of the source text. This task underpins important applications like information retrieval, document comprehension, and event tracking. While much progress has been achieved, state-of-the-art summarization approaches often fail to generate high-quality summaries to reproduce factual details accurately. One of the key limitations of existing solutions is that they are primarily concerned about extracting facts from the source text but overlook other crucial factual information, such as the related time, locations, reasons, consequences, purposes, participants and involved parties. Furthermore, the current summarization frameworks are inadequate in modeling the complex semantic relations among facts and the corresponding factual information, leaving much room for improvement. This paper presents FFSum, a novel summarization framework for exploiting multi-grained factual information to improve text summarization. To this end, FFSum constructs an individual fine-grained factual graph with multiple relations among facts and the corresponding factual information. It employs a fact-driven graph attention network to integrate multi-granular factual representations at the encoding stage. It then uses a hybrid pointer network to retrieve factual pieces from the graph for the summary generation. We evaluate the FFSum by applying it to two real-world datasets. Experimental results show that the FFSum consistently outperforms a state-of-the-art approach across evaluation datasets.
资助项目National Natural Science Foundation of China[U20B2053]
WOS研究方向Acoustics ; Engineering
语种英语
WOS记录号WOS:000795102600003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/49449]  
专题模式识别国家重点实验室_自然语言处理
通讯作者Li, Jianxin
作者单位1.Univ Leeds, Sch Comp, Leeds LS2 9JT, W Yorkshire, England
2.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA
3.CNCERT CC, Beijing 100029, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100045, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100045, Peoples R China
6.Beihang Univ, State Key Lab Software Dev Environm, Beijing 100190, Peoples R China
7.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Mao, Qianren,Li, Jianxin,Peng, Hao,et al. Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2022,30:1665-1678.
APA Mao, Qianren.,Li, Jianxin.,Peng, Hao.,He, Shizhu.,Wang, Lihong.,...&Wang, Zheng.(2022).Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,30,1665-1678.
MLA Mao, Qianren,et al."Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 30(2022):1665-1678.

入库方式： OAI收割

来源：自动化研究所

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