IRAIOct 21, 2024

PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters

arXiv:2410.16148v113 citationsh-index: 53CIKM
Originality Incremental advance
AI Analysis

This work addresses the challenge for podcast listeners in understanding and locating sections in episodes, though it is incremental as it builds on existing transformer methods for segmentation.

The authors tackled the problem of automatically generating chapters for podcast episodes to help listeners navigate long-form audio content, achieving an 11% improvement in ROUGE score over baselines. They also found that auto-generated chapters enhance engagement with less popular podcasts and improve retrieval in search tasks.

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.

Foundations

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