Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?
A benchmark score means nothing without knowing what a trivial method achieves and what the best possible method could achieve. We construct both bounds for a task with a rare kind of ground truth: predicting which sentences a crowd of readers -- highlighting for their own purposes, unpaid, uninstructed, and blind to each other -- marked in 120 web documents. The floor is naive truncation (lead); the ceiling is a split-half oracle: half the crowd predicting the other half. The gap between them is +0.2028 AP [+0.1698, +0.2342, domain-clustered], and three findings structure it. First, the gap is semantic: position and length features recover 5% of it. Second, frontier language models reach...
| Authors | Kazuki Nakayashiki, Keisuke Watanabe |
|---|---|
| Primary category | cs.IR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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