arXiv:2606.11654v1 Announce Type: cross Abstract: 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 sm
The paper directly addresses a known limitation of zero-shot language models in a crucial application area, following prior work that highlighted their struggle with highlight prediction.
Improving AI's ability to predict crowd salience from text alone would significantly enhance content discovery, personalization, and targeted information delivery without relying on historical user data.
The potential to accurately anticipate which parts of a document will engage readers based solely on its raw text, shifting from reactive analysis to proactive content understanding.
- · AI-powered content platforms
- · Publishers and media companies
- · Personalized learning systems
- · Social highlighting tools
- · Platforms relying solely on post-publication engagement data
- · Manual content curation efforts
More efficient and effective content recommendation and summarization systems will emerge.
This could lead to new forms of reader engagement metrics and content valuation based on predicted salience.
The ability to pre-emptively identify 'highlights' could influence content creation itself, optimizing for predicted crowd interest.
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Read at arXiv cs.CL