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Contribution Details

Type Bachelor's Thesis
Scope Discipline-based scholarship
Title Automatic Selection of Illustrative Pictures for News Articles
Organization Unit
  • Nick R. Kipfer
  • Lucien Heitz
  • Luca Rossetto
  • English
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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