Or three short pages if you're optimistic.

Three long pages.

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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.