Paper
15 March 2019 Automatic paper summary generation from visual and textual information
Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, Hirokatsu Kataoka
Author Affiliations +
Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110410U (2019) https://doi.org/10.1117/12.2522789
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
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.
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Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, and Hirokatsu Kataoka "Automatic paper summary generation from visual and textual information", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410U (15 March 2019); https://doi.org/10.1117/12.2522789
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KEYWORDS
Information visualization

Polysomnography

Visualization

Computer vision technology

Machine vision

Artificial intelligence

Data modeling

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