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A simple averaging of multiple models can dramatically enhance sentence prediction accuracy, revealing that the crowd's insights are embedded in document-level structure rather than individual model performance.
A language model can significantly outperform naive truncation in retaining reader-relevant content, achieving a 38.4% retention rate of crowd-marked sentences.
Readers' highlighting preferences are surprisingly stable over time, with personal profiles maintaining predictive power for future selections even after nearly two years.
