Gannett Harnesses Generative AI for Publishing Stories: Should Journalists Be Concerned?
In Brief
Gannett, the largest US newspaper publisher, will integrate generative AI into its publishing system to assist journalists.
A live pilot program using AI will identify key points in articles and generate bullet-point summaries, set to launch at USA Today in Q4.
Journalists will have final authority over AI-generated summaries, with plans to incorporate the technology into the publishing system.
Gannett is developing a generative AI tool to transform long-form stories into various formats, like bullet points or photo captions on slideshows.
The largest newspaper publisher in the US has announced that it will use generative AI in its publishing system for writing stories. Gannett, founded in 1906, said that generative AI would not replace human journalists but rather assist them with some tedious tasks and improve efficiency. The company added that it would have human oversight over the generative AI technology to ensure quality and accuracy.
Gannett, which publishes over 200 daily outlets, soon plans to introduce a live pilot program that uses AI to identify key points within articles and generate concise bullet-point summaries. The feature is set to be launched at USA Today in the fourth quarter of this year. While journalists will retain the authority to decide whether to include AI-generated summaries, Gannett aims to eventually integrate the technology into its publishing system.
The publisher is also working on a generative AI tool designed to transform longer stories into different formats and lengths, such as bullet points or photo captions on engaging slideshows.
To assist in the summarization process, Gannett has partnered with Cohere, a Canadian startup that provides natural language processing models that help companies improve human-machine interactions. Gannett trained Cohere’s extensive language model using 1,000 pre-published stories, which were accompanied by summaries written by its reporters. To improve the quality, journalists from USA Today’s politics team actively participated in the review and editing process of the automated summaries and bullet point highlights.
Gannett has also explored natural language generation (NLG), an AI technique that creates text narratives from factual data to generate stories. Unlike generative AI, NLG does not have thinking capabilities. Journalists review the stories before publishing.
As Reuters further reported, other news outlets like The New York Times and The Washington Post are at different stages of exploring generative AI. In parallel, Bloomberg is developing its own generative AI model trained on financial data, called BloombergGPT. In addition, BBC News Labs is testing semi-automation for creating short-form explainers. Reuters also uses AI for voice-to-text transcription to produce scripts and video subtitles. However, it does not currently publish AI-generated stories, videos, or images.
Gannett Journalists Fight to Protect Their Roles
While generative AI can create efficiencies and eliminate monotonous tasks for journalists, it can also be seen as a threat to their jobs. Gannett journalists are battling to safeguard their positions against AI-driven replacements. Hundreds of employees recently went on strike due to workforce reductions and stagnant wages. However, as mentioned earlier, Gannett said that it is not looking to replace journalists with AI but instead provide a tool for journalists to do their jobs better.
About two weeks ago, Gannett journalists staged a historic walkout, demanding the ouster of CEO Mike Reed for his mismanagement of the company and his excessive pay. The union representing the workers accused Reed of devastating local newsrooms and paying low wages to reporters. The walkout coincided with Gannett’s annual shareholder meeting, where the union urged a no-confidence vote against Reed. After merging with GateHouse in 2019, Gannett, carrying a $1.23 billion debt, laid off over 600 employees.
Generative AI can produce content that is not entirely accurate or reliable. This can be a challenge for industries that require precision and accuracy in their work. The effectiveness of generative AI is directly linked to the quality and diversity of its training data. The more precise and varied the data used for training, the more accurate and diverse the generated output becomes. In addition, generative AI is limited in its ability to generate novel ideas or solutions – this lack of creativity stems from the conventional development approach of AI systems.
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About The Author
Agne is a journalist who covers the latest trends and developments in the metaverse, AI, and Web3 industries for the Metaverse Post. Her passion for storytelling has led her to conduct numerous interviews with experts in these fields, always seeking to uncover exciting and engaging stories. Agne holds a Bachelor’s degree in literature and has an extensive background in writing about a wide range of topics including travel, art, and culture. She has also volunteered as an editor for the animal rights organization, where she helped raise awareness about animal welfare issues. Contact her on [email protected].
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Agne is a journalist who covers the latest trends and developments in the metaverse, AI, and Web3 industries for the Metaverse Post. Her passion for storytelling has led her to conduct numerous interviews with experts in these fields, always seeking to uncover exciting and engaging stories. Agne holds a Bachelor’s degree in literature and has an extensive background in writing about a wide range of topics including travel, art, and culture. She has also volunteered as an editor for the animal rights organization, where she helped raise awareness about animal welfare issues. Contact her on [email protected].
