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|a 10.1109/TVCG.2023.3290241
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|a eng
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|a Liu, Can
|e verfasserin
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|a AutoTitle
|b An Interactive Title Generator for Visualizations
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|c 2024
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|a Date Revised 02.07.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We propose AutoTitle, an interactive visualization title generator satisfying multifarious user requirements. Factors making a good title, namely, the feature importance, coverage, preciseness, general information richness, conciseness, and non-technicality, are summarized based on the feedback from user interviews. Visualization authors need to trade off among these factors to fit specific scenarios, resulting in a wide design space of visualization titles. AutoTitle generates various titles through the process of visualization facts traversing, deep learning-based fact-to-title generation, and quantitative evaluation of the six factors. AutoTitle also provides users with an interactive interface to explore the desired titles by filtering the metrics. We conduct a user study to validate the quality of generated titles as well as the rationality and helpfulness of these metrics
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|a Journal Article
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|a Guo, Yuhan
|e verfasserin
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|a Yuan, Xiaoru
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|t IEEE transactions on visualization and computer graphics
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|g 30(2024), 8 vom: 01. Juli, Seite 5276-5288
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|g year:2024
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|u http://dx.doi.org/10.1109/TVCG.2023.3290241
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