Showing papers by "Toramatsu Shintani published in 2021"
27 Feb 2021
2 citations
TL;DR: In this paper, a lecture video editing system based on augmented reality (AR) technology is proposed to support the work of the lecturer, which is difficult to do by oneself while conducting the lecture, using the information of lecturer's position and progress of the lecture.
Abstract: Assistive technology is a prerequisite for making a high-quality lecture video. It is therefore imperative to edit the lecture video after recording. In this study, we aim to reduce the cumbersome task of lecture video editing by developing a system that enables the addition of visual effects in the video while recording. In particular, we use augmented reality (AR) technology to digitize and display in real-time lecture materials, assistant agents, and other recording contents used by the lecturer. Our system realizes such a mechanism as a lecture recording environment. In addition, our system based on AR technology can support the work of the lecturer, which is difficult to do by oneself while conducting the lecture, using the information of the lecturer's position and the progress of the lecture. We evaluated the system functionality and performance, and verified the system's correct behavior. If the burden of making lecture videos can be reduced, the lecturer will be able to devote more time to improving the quality of lecture contents, which is expected to contribute to the improvement of lectures.
TL;DR: The authors developed an extractive title generation system that formulates titles from keywords extracted from an abstract, and evaluated the appropriateness of paper titles using a BERT-based evaluation model.
Abstract: The formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with titles of top-conference papers by using BERT. This paper describes the training data, implementation, and experimental results. The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students.