Scim: Intelligent Skimming Support for Scientific Papers

TL;DR

This paper presents Scim, an intelligent interface that enhances scientific paper skimming by highlighting diverse, important content in a configurable, faceted manner, aiding researchers in quickly understanding papers.

Contribution

The paper introduces Scim, a novel skimming support tool that highlights salient content with adjustable density and content facets, improving the efficiency of paper review.

Findings

Scim effectively highlights diverse and important content for skimming.

Users found configurable highlights improved skimming efficiency.

Design tensions reveal trade-offs in highlight density and content diversity.

Abstract

Researchers need to keep up with immense literatures, though it is time-consuming and difficult to do so. In this paper, we investigate the role that intelligent interfaces can play in helping researchers skim papers, that is, rapidly reviewing a paper to attain a cursory understanding of its contents. After conducting formative interviews and a design probe, we suggest that skimming aids should aim to thread the needle of highlighting content that is simultaneously diverse, evenly-distributed, and important. We introduce Scim, a novel intelligent skimming interface that reifies this aim, designed to support the skimming process by highlighting salient paper contents to direct a skimmer's focus. Key to the design is that the highlights are faceted by content type, evenly-distributed across a paper, with a density configurable by readers at both the global and local level. We evaluate Scim with an in-lab usability study and deployment study, revealing how skimming aids can support readers throughout the skimming experience and yielding design considerations and tensions for the design of future intelligent skimming tools.