PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters (2410.16148v1)

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.

• The paper presents PODTILE, a transformer-based model that automatically segments and generates titles for podcast episodes, enabling structured navigation.
• PODTILE leverages LongT5 with integrated static and dynamic contexts to enhance both transcript segmentation and title prediction.
• Evaluation shows an 11% ROUGEL improvement over baselines, leading to better chapter coherence and increased user engagement on platforms like Spotify.

Facilitating Podcast Navigation with PODTILE: A Chapterization Model

The paper under review introduces PODTILE, a novel approach for automatically chapterizing podcast episodes. Presented within the context of the ACM International Conference on Information and Knowledge Management, this work addresses the challenges associated with segmenting lengthy and unstructured audio content. The primary contribution lies in the development of a fine-tuned encoder-decoder transformer model that performs both segmentation and title generation for podcast transcripts. This approach seeks to create a structured overview of content, facilitating easier navigation and improving information retrieval.

Technical Framework and Methodology

PODTILE is designed to overcome the inherent challenges in podcast transcripts, such as their length and less structured nature compared to written texts. The model employs LongT5 as a backbone, utilizing a sequence-to-sequence approach for both segmentation and title generation tasks. The model innovatively incorporates two types of global context into the processing of transcripts: static context, derived from podcast metadata, and dynamic context, reflecting intermediate states of the chapterization process. By integrating these contexts, PODTILE aims to improve prediction consistency and context awareness in generated chapters.

Numerical Evaluation

The paper presents a rigorous evaluation of PODTILE against several baselines, including CATS, Gen (seg+label), and GPT-4. On the podcast dataset, PODTILE achieves improvements in title metrics, demonstrating its effectiveness in generating semantically coherent and contextually relevant chapters. Specifically, a noteworthy enhancement of 11% in ROUGEL scores over previous models highlights the model's capacity to generate informative chapter titles.

Implications and Future Directions

From a practical standpoint, the deployment of PODTILE on Spotify's platform has shown promising increases in user engagement, particularly for less popular podcast episodes. This suggests that auto-generated chapters can indeed serve as valuable tools for enhancing user interaction with long-form audio content.

Theoretically, this work advances methodologies in natural language generation by addressing the unique requirements of spoken language processing and highlighting the importance of global context in chapterization tasks. Future research could explore integrating multimodal data, such as audio cues, to further refine the model’s performance.

Conclusion

Overall, PODTILE represents a meaningful contribution to the field of automated content segmentation. It effectively bridges the gap left by the lack of creator-provided chapters for podcasts, offering a scalable solution for content structuring. The implications of this research extend beyond improved navigation, potentially influencing content summarization and retrieval methodologies in spoken content domains.