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Automatic paper summary generation from visual and textual information
Author(s): Shintaro Yamamoto; Yoshihiro Fukuhara; Ryota Suzuki; Shigeo Morishima; Hirokatsu Kataoka
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Paper Abstract

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.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410U (15 March 2019); doi: 10.1117/12.2522789
Show Author Affiliations
Shintaro Yamamoto, Waseda Univ. (Japan)
Yoshihiro Fukuhara, Waseda Univ. (Japan)
Ryota Suzuki, National Institute of Advanced Industrial Science and Technology (Japan)
Shigeo Morishima, Waseda Univ. (Japan)
Hirokatsu Kataoka, National Institute of Advanced Industrial Science and Technology (Japan)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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