  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 available. It is believed that the
proposed method will provide a better way for researchers to stay caught with
important academic papers.
