Journalists must find stories in huge amounts of textual data (e.g. leaks,
bills, press releases) as part of their jobs: determining when and why text
becomes news can help us understand coverage patterns and help us build
assistive tools. Yet, this is challenging because very few labelled links
exist, language use between corpora is very different, and text may be covered
for a variety of reasons. In this work we focus on news coverage of local
public policy in the San Francisco Bay Area by the San Francisco Chronicle.
First, we gather news articles, public policy documents and meeting recordings
and link them using probabilistic relational modeling, which we show is a
low-annotation linking methodology that outperforms other retrieval-based
baselines. Second, we define a new task: newsworthiness prediction, to predict
if a policy item will get covered. We show that different aspects of public
policy discussion yield different newsworthiness signals. Finally we perform
human evaluation with expert journalists and show our systems identify policies
they consider newsworthy with 68% F1 and our coverage recommendations are
helpful with an 84% win-rate.

记者需要从大量文本数据中找到故事，并确定文本何时成为新闻，以了解报道模式并建立辅助工具。本研究聚焦于旧金山湾区的本地公共政策新闻报道，通过概率关系建模进行文本链接，定义了新的任务：新闻价值预测，并进行专家记者的人工评估，得出系统在识别新闻价值并提供报道建议方面的高性能结果。