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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Automatic Selection of Illustrative Pictures for News Articles |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | In this thesis, two different models were implemented for the selection of illustrative images for news articles: the MUSE model and the Xception model. The MUSE model is based on the Multilingual Universal Sentence Encoder, while the Xception model is based on a multi-modal embedding structure building upon the MUSE model. The two models were compared and the MUSE model did perform better in terms of creating useful image recommendations. A user study was conducted for the MUSE model, which produced mixed results. From a developer perspective the DDIS use case requirements were missed, when only considering a single image recommendation. This is due to high variance in quality between the MUSE models image recommendations. If the require- ments are softened slightly, such that a small range of images could be recommended instead of single one, the MUSE model is almost guaranteed to give at least one useful prediction. |
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