新闻文章分析的自动化、操作化和产品化
Automating, Operationalizing and Productizing Journalistic Article Analysis
摘要：
公益软件的产品将新闻文章和其他叙事内容与相关的慈善事业和非营利组织相匹配，以便读者可以就文章出版商提出的问题采取行动。以前这是一个昂贵的劳动密集型过程，机器学习和其他自动文本分析的应用现在允许我们将这个匹配过程扩展到多个大型国家媒体每天产生的内容量。本文描述了一个跨一般新闻模型的分层策略系统的开发，该系统最大限度地减少了对人工策展的需求，同时保持了对每个出版物的关注。我们提出了一些一般的策略，分类异质文本，并建议编辑和操作策略的出版商，使他们的出版物和个人的内容项目更有效地分析自动化系统。
英文摘要：
Public Good Software's products match journalistic articles and other narrative content to relevant charitable causes and nonprofit organizations so that readers can take action on the issues raised by the articles' publishers. Previously an expensive and labor-intensive process, application of machine learning and other automated textual analyses now allow us to scale this matching process to the volume of content produced daily by multiple large national media outlets. This paper describes the development of a layered system of tactics working across a general news model that minimizes the need for human curation while maintaining the particular focus of concern for each individual publication. We present a number of general strategies for categorizing heterogenous texts, and suggest editorial and operational tactics for publishers to make their publications and individual content items more efficiently analyzed by automated systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
