Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization

doi:10.1109/ACCESS.2024.3388493

	Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization
	Ma, Bing 1,2
	2024
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	12 页码:54371-54381
摘要	The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original multiple documents and satisfies content diversity. To fulfill the dual requirements of coverage and diversity in multi-document summarization, this study introduces a novel method. Initially, a class tree is constructed through hierarchical clustering of documents. Subsequently, a sentence selection method based on class tree is proposed for generating a summary. Specifically, a top-down traversal is performed on the class tree, during which sentences are selected from each node based on their similarity to the centroid of the documents within the node and their dissimilarity to the centroid of documents not belonging to the node. Sentences selected from the root node reflect the commonality of all document, and sentences selected from the sub nodes reflect the distinct specificity of the respective subclasses. Experimental results on standard text summarization datasets DUC'2002, DUC'2003, and DUC'2004 demonstrate that the proposed method significantly outperforms the variant method that considers only commonality of all documents, achieving average improvements of up to 1.54 and 1.42 in ROUGE-1 and ROUGE-L scores, respectively. Additionally, the method demonstrates significant superiority over another variant method that considers only the specificity of subclasses, achieving average improvements of up to 2.16 and 2.01 in ROUGE-1 and ROUGE-L scores, respectively. Furthermore, extensive experiments on DUC'2004 and Multi-News datasets show that the proposed method outperforms lots of competitive supervised and unsupervised multi-document summarization methods and yields considerable results.
关键词	Vectors Task analysis Feature extraction Natural languages Clustering algorithms Symmetric matrices Standards Class tree commonality and specificity hierarchical clustering of documents multi-document summarization pre-trained embedding representation
DOI	10.1109/ACCESS.2024.3388493
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001205751700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38709
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, Bing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Ma, Bing. Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization[J]. IEEE ACCESS,2024,12:54371-54381.
APA	Ma, Bing.(2024).Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization.IEEE ACCESS,12,54371-54381.
MLA	Ma, Bing."Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization".IEEE ACCESS 12(2024):54371-54381.