论文标题
如果流血，它会导致：一种涵盖洛杉矶犯罪的计算方法
If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles
论文作者
论文摘要
开发和改善涵盖新闻的计算方法可以增加新闻成果并改善故事的涵盖方式。在这项工作中，我们解决了涵盖洛杉矶犯罪故事的问题。我们提出了一个机器中的系统，该系统涵盖了个人犯罪，（1）从古典新闻文章中学习原型覆盖型原型，以学习其结构，以及（2）使用洛杉矶警察局的输出来产生“ Lede段落”，这是犯罪文章的第一个结构性单位。我们介绍了一种用于学习文章结构的概率图形模型，以及一个基于规则的系统来生成LEDES。我们希望我们的工作能够导致将这些组件一起使用的系统，以形成涉及犯罪的新闻文章的骨骼。 这项工作是针对乔纳森·梅（Jonathan May）的高级自然语言处理课程的集体项目完成的，2019年秋季。
Developing and improving computational approaches to covering news can increase journalistic output and improve the way stories are covered. In this work we approach the problem of covering crime stories in Los Angeles. We present a machine-in-the-loop system that covers individual crimes by (1) learning the prototypical coverage archetypes from classical news articles on crime to learn their structure and (2) using output from the Los Angeles Police department to generate "lede paragraphs", first structural unit of crime-articles. We introduce a probabilistic graphical model for learning article structure and a rule-based system for generating ledes. We hope our work can lead to systems that use these components together to form the skeletons of news articles covering crime. This work was done for a class project in Jonathan May's Advanced Natural Language Processing Course, Fall, 2019.
