By automating chapter creation, Spotify’s PODTILE makes finding the best moments in long podcast episodes easier than ever, revolutionizing content discovery and boosting listener engagement.
Research: PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters. Image Credit: Kaspars Grinvalds / Shutterstock
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
In a recent study published on the arXiv preprint* server, Spotify researchers introduced PODTILE, a novel encoder-decoder transformer model designed to automate chapter creation for podcast episodes on the Spotify platform. It segments conversational data into semantically coherent chapters with titles and timestamps, addressing the challenge of structuring long, unstructured conversations. The aim was to improve user engagement by organizing episode content into easily navigable segments.
The model incorporates global context, such as episode metadata and previous chapter titles, to improve the accuracy and relevance of chapters and help users easily find specific sections. Findings from the study showed that auto-generated chapters were useful for lesser-known podcasts and enhanced search results, demonstrating PODTILE’s practical utility for both content discovery and user retention.
Podcast Content Segmentation
Chapterization, the process of dividing content into segments with relevant titles, has been widely recognized for improving navigation and retrieval. While traditional methods focused on structured texts like Wikipedia articles and news, the rise of spoken content, like podcasts, requires automated solutions capable of handling dynamic, long-form audio content.
Podcasts, with their spontaneous discussions and nuanced transitions, present unique challenges for maintaining context over long transcripts. Traditional methods, like the text-to-text transformer for long sequences (LongT5), struggle with the long token lengths typical of podcast transcripts, highlighting the need for new models that can balance efficiency with accuracy.
PODTILE: Podcast Segmentation Technique
In this paper, the authors proposed PODTILE, a fine-tuned encoder-decoder transformer model specifically designed to segment and title podcast episodes. Their model simultaneously generates chapter transitions and titles for input transcripts while maintaining context using global information such as the episode’s title, description, and previous chapter titles. This helps address the challenge of processing long, unstructured podcast transcripts, which average around 16,000 tokens.
PODTILE was built using the LongT5 model, which balances efficiency and power. The model processes input text in chunks, utilizing static context (episode metadata) and dynamic context (previous chapter titles) to enhance chapter prediction accuracy. This enables effective handling of long and unstructured content without solely relying on the computational power of large language models (LLMs).
The researchers employed a sliding, non-overlapping window technique to process the input text within the model’s context window. They trained the model with supervised data, adding index numbers before each sentence to facilitate chapter boundary predictions. Experiments were conducted using three datasets: an internal podcast dataset with 10.8k episodes, WikiSection, and QMSum. The podcast episodes had chapters ranging from 30 seconds to 30 minutes and titles shorter than 15 words. WikiSection and QMSum provide conversational data to validate model effectiveness across different domains.
Key Findings and Insights
The outcomes showed that incorporating global context into the input text significantly improved the quality of chapter titles, especially for longer documents in conversational datasets. PODTILE’s evaluation indicated an 11% increase in ROUGE scores compared to the previous best baseline, highlighting the model’s ability to outperform existing solutions.
The model’s ability to generate accurate, contextually relevant chapter titles was beneficial for longer documents that exceeded the model’s context size. The authors also highlighted the practical benefits of auto-generated chapters for users, especially in navigating less popular podcasts. This improvement was particularly noted in datasets where episodes had varied structures and topic shifts, further emphasizing the model’s adaptability.
The researchers conducted an ablation study to assess the individual effects of static and dynamic context on title quality. The results showed that static context (episode metadata) had a greater impact on title quality, while dynamic context (previous chapter titles) improved title consistency across episodes.
Additionally, longer documents benefited more from a global context, with significant improvements in title quality for documents processed in chunks. User data confirmed these findings, demonstrating increased engagement with segmented content. Deploying PODTILE on the platform demonstrated that users found auto-generated chapters helpful for browsing.
Usage statistics indicated an 88.12% increase in chapter-initiated plays after the model's introduction. Furthermore, auto-generated chapters were particularly beneficial for less popular shows, enhancing user engagement, improving episode descriptions with concise summaries, and also improved search task performance, demonstrating the model’s capability for information retrieval.
Applications
This research has significant implications for enhancing user navigation and engagement with podcast content. The model helps users efficiently browse and locate specific sections within episodes by providing structured chapters with relevant titles. This is particularly useful for less popular podcasts, where auto-generated chapters can significantly improve user engagement.
Additionally, the study explored the potential of using chapter titles as summaries to enhance episode descriptions and improve search effectiveness. By indexing concise chapter titles instead of entire transcripts, platforms can reduce storage costs and enhance information retrieval. The findings suggest that indexing chapter titles instead of full transcripts can reduce costs and improve retrieval performance, making it a practical solution for large-scale content platforms.
Conclusion and Future Scope
In summary, PODTILE improved the automated chapterization of podcast episodes. Its ability to generate accurate chapter transitions and titles and its efficient handling of long, unstructured content make it a valuable tool for enhancing user engagement and information retrieval.
Future work should incorporate multimodal data, such as audio and video, to further improve chapterization. Developing reference-free evaluation metrics may also provide a more comprehensive assessment of the model’s performance. In addition, future versions of the model may explore integrating user feedback to refine the accuracy and relevance of auto-generated chapters. Overall, findings highlighted the effectiveness of PODTILE in enhancing user engagement and search retrieval, providing a practical solution for the growing volume of spoken content.
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
Journal reference:
- Preliminary scientific report.
Ghazimatin, A., & et al. PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters. arXiv, 2024, 2410, 16148. DOI: 10.48550/arXiv.2410.16148, https://arxiv.org/abs/2410.16148