A social highlighter's most useful signal -- which passages a crowd of readers marks -- exists only for documents people have already read. Can the aggregate crowd salience of a document be predicted from its text before its marks accumulate? Prior work on this data found that zero-shot language models recover highlight locations worse than a trivial lead (position) baseline, so we ask whether a model trained on the highlight corpus can beat that baseline. Using a pre-registered ladder of models and a by-document cluster bootstrap, we find a small but robust edge: a logistic ranker over sentence embeddings and positional/contextual features beats the lead baseline by +0.044 average precision (95% CI [+0.029, +0.058]; clears a pre-registered margin delta=0.03 in 97% of resamples, and stable across pipeline re-runs). Two unsupervised extractive baselines (centroid, LexRank-style centrality) lose to lead, and the trained model beats them by +0.108, so the edge is not recovered by generic unsupervised proxies -- it reflects learning from real reader marks. In product terms, precision@3 rises from 0.25 to 0.39 (+55% relative) and the model beats lead on 69% of documents. An ablation attributes the edge to the raw embedding (+0.014) and training augmentation (+0.010), each with a positive CI. The edge is not a temporal-generalization failure, and we find no evidence that content drift or near-duplicate leakage explains it. A standardized regression shows the advantage is governed mainly by document popularity (lower popularity, larger edge) and by label reliability. It nearly vanishes only on the most popular content; there it is the lead baseline that strengthens, not the model that weakens. Because our evaluation conditions on documents that eventually accumulated readers, these results are a retrospective cold-start simulation.

翻译：摘要：社交高亮器最有用的信号——读者群体标记的段落——仅存在于已被阅读的文档中。能否在标记累积之前，从文本中预测文档的聚合群体显著性？此前关于该数据的研究发现，零样本语言模型恢复高亮位置的效果甚至不如简单的起始（位置）基线。因此，我们探究基于高亮语料库训练的模型能否超越该基线。通过使用预注册的模型阶梯和按文档聚类的自助法，我们发现了一个微小但稳健的优势：基于句子嵌入及位置/上下文特征的对数几率排序器，其平均精度比起始基线高出+0.044（95%置信区间[+0.029, +0.058]；在97%的重采样中超过了预注册的边际差δ=0.03，并且在多次流水线重运行中保持稳定）。两种无监督的抽取式基线（质心法、LexRank式中心性）均逊于起始基线，而训练模型比它们高出+0.108，因此这一优势并非由通用无监督代理恢复——它反映了从真实读者标记中的学习。在产品术语中，精确率@3从0.25提升至0.39（相对提升55%），且模型在69%的文档上优于起始基线。消融实验将优势归因于原始嵌入（+0.014）和训练增强（+0.010），各具正置信区间。该优势并非时间泛化失败，我们也未发现内容漂移或近重复泄露的证据。标准化回归显示，优势主要受文档流行度（流行度越低，优势越大）和标签可靠性支配。仅在最具流行度的内容上，优势近乎消失；此时是起始基线增强，而非模型减弱。由于我们的评估条件限定于最终积累读者的文档，这些结果属于回顾性冷启动模拟。