# If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles

arXiv: https://arxiv.org/abs/2206.07115 · HTML: https://mlsanity.com/paper/2206.07115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://mlsanity.com/paper/1807.01270/) (2018)

## Built on by (most cited first, 1 of 1)

- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)

## Abstract (arXiv)

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.