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arXiv · 2505.23908

Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems

Winstead Zhu·Ann Clifton·Azin Ghazimatin·Edgar Tanaka·Edward Ronan
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Abstract

Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a real-world application. Comprehensive offline evaluations and online A/B testing demonstrate that LLM-generated previews consistently outperform a strong baseline built on top of various ML expert models, showcasing a significant reduction in the need for meticulous feature engineering. The offline results indicate notable enhancements in understandability, contextual clarity, and interest level, and the online A/B test shows a 4.6% increase in user engagement with preview content, along with a 5x boost in processing efficiency, offering a more streamlined and performant solution compared to the strong baseline of feature-engineered expert models.

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Winstead Zhu, Ann Clifton, Azin Ghazimatin, Edgar Tanaka, Edward Ronan. 2025-05-29. Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems. https://arxiv.org/abs/2505.23908

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