IEICE TRANSACTIONS on Information
Assessment System of Presentation Slide Design Using Visual and Structural Features
Summary :
Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.3 pp.587-596
- Publication Date
- 2022/03/01
- Publicized
- 2021/12/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021HCK0001
- Type of Manuscript
- Special Section PAPER (Special Section on Human Communication IV)
- Category
Authors
Shengzhou YI
The University of Tokyo
Junichiro MATSUGAMI
Rubato Co., Ltd.
Toshihiko YAMASAKI
The University of Tokyo
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Shengzhou YI, Junichiro MATSUGAMI, Toshihiko YAMASAKI, "Assessment System of Presentation Slide Design Using Visual and Structural Features" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 587-596, March 2022, doi: 10.1587/transinf.2021HCK0001.
Abstract: Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021HCK0001/_p
Copy
@ARTICLE{e105-d_3_587,
author={Shengzhou YI, Junichiro MATSUGAMI, Toshihiko YAMASAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Assessment System of Presentation Slide Design Using Visual and Structural Features},
year={2022},
volume={E105-D},
number={3},
pages={587-596},
abstract={Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.},
keywords={},
doi={10.1587/transinf.2021HCK0001},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Assessment System of Presentation Slide Design Using Visual and Structural Features
T2 - IEICE TRANSACTIONS on Information
SP - 587
EP - 596
AU - Shengzhou YI
AU - Junichiro MATSUGAMI
AU - Toshihiko YAMASAKI
PY - 2022
DO - 10.1587/transinf.2021HCK0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.
ER -
English
