Accelerating Scientific Paper Skimming with Augmented Intelligence Through Customizable Faceted Highlights
Raymond Fok, Luca Soldaini, Cassidy Trier, Erin Bransom, Kelsey MacMillan, Evie Yu-Yen Cheng, Hita Kambhamettu, Jonathan Bragg, Kyle Shih-Huang Lo, Marti A. Hearst, Andrew J. Head, Daniel S. Weld · ACM Transactions on Interactive Intelligent Systems · 2024
Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim , a novel intelligent interface that helps scholars skim papers to rapidly review and gain a cursory understanding of its contents. Scim supports the skimming process by highlighting salient content within a paper, directing a scholar’s attention. These automatically-extracted highlights are faceted by content type, evenly-distributed across a paper, and have a density configurable by scholars. We evaluate Scim with an in-lab usability study and a longitudinal diary study, revealing how its highlights facilitate the more efficient construction of a conceptualization of a paper. Finally, we describe the process of scaling highlights from their conception within Scim , a research prototype, to production on over 521,000 papers within the Semantic Reader, a publicly-available augmented reading interface for scientific papers. We conclude by discussing design considerations and tensions for the design of future skimming tools with augmented intelligence.