Modeling "Newsworthiness" for Lead-Generation Across Corpora

• URL: http://arxiv.org/abs/2104.09653v1
• Date: Mon, 19 Apr 2021 21:48:15 GMT
• Title: Modeling "Newsworthiness" for Lead-Generation Across Corpora
• Authors: Alexander Spangher, Nanyun Peng, Jonathan May and Emilio Ferrara
• Abstract summary: We train models on automatically labeled corpora to predict whether each article was a front-page article. We rank documents in unlabeled corpora on "newsworthiness" A fine-tuned RoBERTa model achieves.93 AUC performance on heldout labeled documents, and.88 AUC on expert-validated unlabeled corpora.
• Score: 85.92467549469147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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