  Journalists obtain "leads", or story ideas, by reading large corpora of
government records: court cases, proposed bills, etc. However, only a small
percentage of such records are interesting documents. We propose a model of
"newsworthiness" aimed at surfacing interesting documents. We train models on
automatically labeled corpora -- published newspaper articles -- to predict
whether each article was a front-page article (i.e., \textbf{newsworthy}) or
not (i.e., \textbf{less newsworthy}). We transfer these models to unlabeled
corpora -- court cases, bills, city-council meeting minutes -- to rank
documents in these corpora on "newsworthiness". A fine-tuned RoBERTa model
achieves .93 AUC performance on heldout labeled documents, and .88 AUC on
expert-validated unlabeled corpora. We provide interpretation and visualization
for our models.
