Ut sit amet diam suscipit mauris ornare aliquam.

Suspendisse eget mauris eu tellus molestie cursus.

In mauris.

Fusce vulputate ipsum a mauris.

Integer id quam.

In vel mi sit amet augue congue elementum.

Integer euismod lacus luctus magna.

Integer nec odio.

In vel mi sit amet augue congue elementum.

Integer lacinia sollicitudin massa.

Integer euismod lacus luctus magna.

In scelerisque sem at dolor.

Integer nec odio.

In scelerisque sem at dolor.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

AbstractExplorer instantiates new minimally lossy SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

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.

We contribute: • Novel SMT theory-informed text analysis and rendering techniques for enabling cross-document skimming and comparative close reading at scale • AbstractExplorer, which instantiates these techniques for familiarizing oneself with a corpus of ∼1000 CHI paper abstracts. • Three studies informing and evalutaing the benefits, challenges, and interactions between these techniques.