Measuring Alignment With Reader Highlights Net of Position and Length
Context compression discards most of a document before a language model reads it, and is normally evaluated by downstream task accuracy - which makes another model the judge of what mattered. Naturalistic social highlighting offers a non-circular reference: many people independently marking passages on the same page. But the obvious metric, the fraction of crowd-marked sentences a compressor keeps, is confounded twice: crowd marks are front-loaded and crowd-marked sentences are longer, so any method favouring early or long sentences scores well regardless of readers. We remove both by matching each marked sentence against unmarked sentences of the same document at equal relative depth and...
| Authors | Kazuki Nakayashiki, Keisuke Watanabe |
|---|---|
| Primary category | cs.IR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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