Rich-text document styling restoration via reinforcement learning

doi:10.1007/s11704-020-9322-7

	Rich-text document styling restoration via reinforcement learning
	Li, Hongwei 1,2; Hu, Yingpeng 1,2; Cao, Yixuan 1,2; Zhou, Ganbin 3; Luo, Ping 1,2
	2021-08-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	15 期号:4 页码:11
摘要	Richly formatted documents, such as financial disclosures, scientific articles, government regulations, widely exist on Web. However, since most of these documents are only for public reading, the styling information inside them is usually missing, making them improper or even burdensome to be displayed and edited in different formats and platforms. In this study we formulate the task of document styling restoration as an optimization problem, which aims to identify the styling settings on the document elements, e.g., lines, table cells, text, so that rendering with the output styling settings results in a document, where each element inside it holds the (closely) exact position with the one in the original document. Considering that each styling setting is a decision, this problem can be transformed as a multi-step decision-making task over all the document elements, and then be solved by reinforcement learning. Specifically, Monte-Carlo Tree Search (MCTS) is leveraged to explore the different styling settings, and the policy function is learnt under the supervision of the delayed rewards. As a case study, we restore the styling information inside tables, where structural and functional data in the documents are usually presented. Experiment shows that, our best reinforcement method successfully restores the stylings in 87.65% of the tables, with 25.75% absolute improvement over the greedy method. We also discuss the tradeoff between the inference time and restoration success rate, and argue that although the reinforcement methods cannot be used in real-time scenarios, it is suitable for the offline tasks with high-quality requirement. Finally, this model has been applied in a PDF parser to support cross-format display.
关键词	styling restoration monte-carlo tree search reinforcement learning richly formatted documents tables
DOI	10.1007/s11704-020-9322-7
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[U1811461] ; Innovation Program of Institute of Computing Technology, CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000729649100001
出版者	HIGHER EDUCATION PRESS
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://119.78.100.204/handle/2XEOYT63/18085
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Ping
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 100049, Peoples R China 3.Tencent, WeChat Search Applicat Dept, Search Prod Ctr, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Li, Hongwei,Hu, Yingpeng,Cao, Yixuan,et al. Rich-text document styling restoration via reinforcement learning[J]. FRONTIERS OF COMPUTER SCIENCE,2021,15(4):11.
APA	Li, Hongwei,Hu, Yingpeng,Cao, Yixuan,Zhou, Ganbin,&Luo, Ping.(2021).Rich-text document styling restoration via reinforcement learning.FRONTIERS OF COMPUTER SCIENCE,15(4),11.
MLA	Li, Hongwei,et al."Rich-text document styling restoration via reinforcement learning".FRONTIERS OF COMPUTER SCIENCE 15.4(2021):11.