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.
@article{arxiv.2104.09653,
title = {Modeling "Newsworthiness" for Lead-Generation Across Corpora},
author = {Alexander Spangher and Nanyun Peng and Jonathan May and Emilio Ferrara},
journal= {arXiv preprint arXiv:2104.09653},
year = {2021}
}