Accelerating Scientific Paper Skimming with Augmented Intelligence Through Customizable Faceted Highlights-Reference-Cited by-同舟云学术

Accelerating Scientific Paper Skimming with Augmented Intelligence Through Customizable Faceted Highlights

Published:2024-05-23 Issue: Volume: Page:
ISSN:2160-6455
Container-title:ACM Transactions on Interactive Intelligent Systems
language:en
Short-container-title:ACM Trans. Interact. Intell. Syst.

Author:

Fok Raymond¹^ORCID,Soldaini Luca²^ORCID,Trier Cassidy²^ORCID,Bransom Erin²^ORCID,MacMillan Kelsey²^ORCID,Cheng Evie²^ORCID,Kambhamettu Hita³^ORCID,Bragg Jonathan²^ORCID,Lo Kyle²^ORCID,Hearst Marti A.⁴^ORCID,Head Andrew³^ORCID,Weld Daniel S.⁵^ORCID

Affiliation:

1. University of Washington, USA

2. Allen Institute for AI, USA

3. University of Pennsylvania, USA

4. UC Berkeley, USA

5. Allen Institute for AI & University of Washington, USA

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3665648

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