Automatic Generation of Survey Paper Based on Template Tree

Xiaoping Sun, Hai Zhuge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With rapid expansion of scientific papers, making a survey from a large collection of papers on a given research issue or domain becomes more and more important for researchers. This paper proposes a template-based framework for automatically generating survey paper. It allows users to compose a template tree as a syllabus for the required survey. Each tree node corresponds to a section to be composed in the survey therefore the whole tree defines the section structure of the survey. The template consists of two types of nodes, dimension node and topic node, which filter contents of papers. A recursive procedure along the survey generation template tree paths is conducted to process documents, rank sentences, and compose sections. We apply the approach to generating the survey of the reference papers of a survey paper and compare the result with the survey paper. Experiments show improvement over several baseline methods.
Original languageEnglish
Title of host publication2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
PublisherIEEE
Pages89-96
ISBN (Electronic)978-1-7281-5823-5
ISBN (Print)978-1-7281-5824-2
DOIs
Publication statusPublished - 23 Mar 2020
Event2019 15th International Conference on Semantics, Knowledge and Grids (SKG) - Guangzhou, China
Duration: 17 Sep 201918 Sep 2019

Publication series

Name2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
PublisherIEEE
ISSN (Print)2325-0623

Conference

Conference2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
Period17/09/1918/09/19

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Cite this

Sun, X., & Zhuge, H. (2020). Automatic Generation of Survey Paper Based on Template Tree. In 2019 15th International Conference on Semantics, Knowledge and Grids (SKG) (pp. 89-96). (2019 15th International Conference on Semantics, Knowledge and Grids (SKG)). IEEE. https://doi.org/10.1109/SKG49510.2019.00023