A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization

doi:10.3390/electronics12102184

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization
	Zhao, Henghui 1; Zhang, Wensheng1,2 ; Huang, Mengxing 1; Feng, Siling 1; Wu, Yuanyuan 1
发表期刊	ELECTRONICS
	2023-05-10
卷号	12 期号:10 页码:12
通讯作者	Zhang, Wensheng(wensheng.zhang@ia.ac.cn) ; Huang, Mengxing(huangmx09@hainanu.edu.cn)
摘要	Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.
关键词	graph neural network heterogeneous graph attention mechanism implicit topic
DOI	10.3390/electronics12102184
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[82260362] ; National Natural Science Foundation of China[62241202] ; National Key R&D Program of China[2021ZD0111000]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:000996247200001
出版者	MDPI
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53385
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Zhang, Wensheng; Huang, Mengxing
作者单位	1.Hainan Univ, Sch Informat & Commun Engn, Haikou 570100, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhao, Henghui,Zhang, Wensheng,Huang, Mengxing,et al. A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization[J]. ELECTRONICS,2023,12(10):12.
APA	Zhao, Henghui,Zhang, Wensheng,Huang, Mengxing,Feng, Siling,&Wu, Yuanyuan.(2023).A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization.ELECTRONICS,12(10),12.
MLA	Zhao, Henghui,et al."A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization".ELECTRONICS 12.10(2023):12.