PAW Business OPERATIONAL
Case Study: Barron’s Group
Learn how Barron’s Group is leveraging automation and Machine Learning to produce direct-to-reader content and power dynamic editorial tools. This talk will focus on both the technical infrastructure necessary to automate content, and the innumerable product and editorial decisions made throughout the design, development, and deployment phases.
Over the past year, Barron’s Group has used Wordsmith to manage the text generation component of its automated story systems. Auto-generated stories are templated to summarize relevant stock-market events, and may be triggered within our own system to publish under any conditions specified. Because templates can be designed for any relevant use-case, this system is easily extended to different story topics, languages, and business segments. Barron’s currently produces stories on the state of the market at company close, and plans to expand to publish japanese-language translations, automated flash headlines, and morning stock summaries.
Barron’s Group is also in the process of building a Machine Learning-powered system for editorial assists. This tool will rely on anomaly detection performed with Amazon Sagemaker, and will alert editors when noteworthy events occur. In the short-term, this tool is intended as a time-saver for editors who would otherwise have to manually sift through price and volume data for dozens of stocks. In the long term, this kind of technology could be combined with Wordsmith’s templating functionality to produce entire headlines or articles to send off to editors, for review or publication. Here, we will focus primarily on our current process for automated article publication with Wordsmith, along with our work to this point on the Sagemaker editorial assist tool. We will review the results of our work, and discuss future steps that can be taken to expand upon these systems.
Software Engineer - ML Automation
Dow Jones & Company, Inc.