Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application

doi:10.1007/s11390-021-1076-7

	Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application
	Cao, Rong-Yu 1,2; Cao, Yi-Xuan 1,2; Zhou, Gan-Bin 3; Luo, Ping 1,2,4
	2022-06-01
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	37 期号:3 页码:699-718
摘要	In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications (e.g., passage-based retrieval and high-quality information extraction). However, long documents, containing hundreds or even thousands of pages and a variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper position on the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. As for evaluations, we find that previous studies ignore the error that the depth of a node is correct while its path to the root is wrong. Since such mistakes may worsen the downstream applications seriously, a new measure is developed for a more careful evaluation. The empirical experiments based on thousands of long documents from Chinese financial market, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972 6, 0.729 1 and 0.957 8 in the Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.
关键词	logical document hierarchy long document passage retrieval
DOI	10.1007/s11390-021-1076-7
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China[62076231] ; National Natural Science Foundation of China[U1811461]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:000812520400015
出版者	SCIENCE PRESS
引用统计	被引频次：3[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/2XEOYT63/19531
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cao, Rong-Yu
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Tencent Holdings Ltd, WeChat Search Applicat Dept, Beijing 100080, Peoples R China 4.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式 GB/T 7714	Cao, Rong-Yu,Cao, Yi-Xuan,Zhou, Gan-Bin,et al. Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2022,37(3):699-718.
APA	Cao, Rong-Yu,Cao, Yi-Xuan,Zhou, Gan-Bin,&Luo, Ping.(2022).Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,37(3),699-718.
MLA	Cao, Rong-Yu,et al."Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 37.3(2022):699-718.