Automatic paper summary generation from visual and textual information

Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, Hirokatsu Kataoka

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

1 Citation (Scopus)


Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts. In response to this situation, this paper proposes the paper summary generation (PSG) task using a simple but effective method to automatically generate an academic paper summary from raw PDF data. We realized PSG by combination of vision-based supervised components detector and language-based unsupervised important sentence extractor, which is applicable for a trained format of manuscripts. We show the quantitative evaluation of ability of simple vision-based components extraction, and the qualitative evaluation that our system can extract both visual item and sentence that are helpful for understanding. After processing via our PSG, the 979 manuscripts accepted by the Conference on Computer Vision and Pattern Recognition (CVPR) 2018 are available1. It is believed that the proposed method will provide a better way for researchers to stay caught with important academic papers.

Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision, ICMV 2018
EditorsDmitry P. Nikolaev, Antanas Verikas, Petia Radeva, Jianhong Zhou
ISBN (Electronic)9781510627482
Publication statusPublished - 2019
Event11th International Conference on Machine Vision, ICMV 2018 - Munich, Germany
Duration: 2018 Nov 12018 Nov 3

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference11th International Conference on Machine Vision, ICMV 2018


  • Document Analysis
  • Paper Summarization
  • Vision and Language

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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