An End-to-End Workflow using Topic Segmentation and Text Summarisation Methods for Improved Podcast Comprehension

TL;DR

This paper investigates how combining topic segmentation and text summarisation can enhance podcast comprehension, demonstrating effective methods and models that produce concise, relevant summaries aligned closely with human titles.

Contribution

It introduces a novel workflow integrating TextTiling, TextSplit, and summarisation models T5, BART, Pegasus for improved podcast episode summarisation.

Findings

TextSplit achieved the best segmentation metrics.

T5 produced the most relevant summaries.

Summaries were only 8% less relevant than human titles.

Abstract

The consumption of podcast media has been increasing rapidly. Due to the lengthy nature of podcast episodes, users often carefully select which ones to listen to. Although episode descriptions aid users by providing a summary of the entire podcast, they do not provide a topic-by-topic breakdown. This study explores the combined application of topic segmentation and text summarisation methods to investigate how podcast episode comprehension can be improved. We have sampled 10 episodes from Spotify's English-Language Podcast Dataset and employed TextTiling and TextSplit to segment them. Moreover, three text summarisation models, namely T5, BART, and Pegasus, were applied to provide a very short title for each segment. The segmentation part was evaluated using our annotated sample with the $P_k$ and WindowDiff ($WD$) metrics. A survey was also rolled out ($N=25$) to assess the quality of the generated summaries. The TextSplit algorithm achieved the lowest mean for both evaluation metrics ($\bar{P_k}=0.41$ and $\bar{WD}=0.41$), while the T5 model produced the best summaries, achieving a relevancy score only $8\%$ less to the one achieved by the human-written titles.