PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters

TL;DR

PODTILE is a transformer-based model that automatically segments podcast transcripts into coherent chapters with titles, improving navigation and searchability for long-form audio content.

Contribution

We introduce PODTILE, a novel encoder-decoder transformer that generates chapter segments and titles from lengthy, unstructured podcast transcripts, addressing scalability and context preservation challenges.

Findings

11% ROUGE score improvement over baseline

Auto-generated chapters aid listener navigation

Chapter titles enhance search retrieval effectiveness

Abstract

Listeners of long-form talk-audio content, such as podcast episodes, often find it challenging to understand the overall structure and locate relevant sections. A practical solution is to divide episodes into chapters--semantically coherent segments labeled with titles and timestamps. Since most episodes on our platform at Spotify currently lack creator-provided chapters, automating the creation of chapters is essential. Scaling the chapterization of podcast episodes presents unique challenges. First, episodes tend to be less structured than written texts, featuring spontaneous discussions with nuanced transitions. Second, the transcripts are usually lengthy, averaging about 16,000 tokens, which necessitates efficient processing that can preserve context. To address these challenges, we introduce PODTILE, a fine-tuned encoder-decoder transformer to segment conversational data. The model simultaneously generates chapter transitions and titles for the input transcript. To preserve context, each input text is augmented with global context, including the episode's title, description, and previous chapter titles. In our intrinsic evaluation, PODTILE achieved an 11% improvement in ROUGE score over the strongest baseline. Additionally, we provide insights into the practical benefits of auto-generated chapters for listeners navigating episode content. Our findings indicate that auto-generated chapters serve as a useful tool for engaging with less popular podcasts. Finally, we present empirical evidence that using chapter titles can enhance effectiveness of sparse retrieval in search tasks.