CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context

February 14, 2023 Β· Declared Dead Β· πŸ› International Conference on Human Factors in Computing Systems

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Authors Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld arXiv ID 2302.07302 Category cs.HC: Human-Computer Interaction Cross-listed cs.DL Citations 54 Venue International Conference on Human Factors in Computing Systems Last Checked 3 months ago
Abstract
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user's publishing, reading, and saving activities to provide personalized visual augmentations and context around citations. First, CiteSee connects the current paper to familiar contexts by surfacing known citations a user had cited or opened. Second, CiteSee helps users prioritize their exploration by highlighting relevant but unknown citations based on saving and reading history. We conducted a lab study that suggests CiteSee is significantly more effective for paper discovery than three baselines. A field deployment study shows CiteSee helps participants keep track of their explorations and leads to better situational awareness and increased paper discovery via inline citation when conducting real-world literature reviews.
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