AbstractExplorer: Leveraging Structure-Mapping Theory to Enhance Comparative Close Reading at Scale

DOI

Individual flagship conferences today can have over a thousand papers; even reading just the abstract of every paper at the latest relevant conference to keep up with the research is time and memory prohibitive. Previous visualizations in this domain have ubiquitously followed Shneiderman's Visual Information-Seeking Mantra, with details available on demand. However, recently in other domains, system designers have leveraged Structure-Mapping Theory (SMT) to facilitate seeing both the overview and the details at the same time, facilitating abstraction without losing context. We compose and evaluate a system, called AbstractExplorer, with analogous SMT-derived characteristics for the domain of scientific abstract corpus familiarization. AbstractExplorer has a unique combination of LLM-powered (1) faceted comparative close reading with (2) role highlighting enhanced by (3) structure-based ordering and (4) alignment. An ablation study (N=24) validated that these features work best together. A summative study (N=16) describes how these features support users in familiarizing themselves with a corpus of paper abstracts from a single large conference with over 1000 papers.