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.

翻译：播客媒体的消费量持续快速增长。由于播客单集篇幅较长，用户通常会精心选择收听内容。尽管单集描述通过提供整集摘要帮助用户，但未能实现按主题逐段分解。本研究探索了主题分割与文本摘要方法的联合应用，以探究如何提升播客单集内容理解。我们从Spotify英语播客数据集中采样10个单集，采用TextTiling和TextSplit进行分割。同时，应用三种文本摘要模型（T5、BART和Pegasus）为每个段落生成极短标题。分割部分使用带标注样本结合$P_k$和WindowDiff（$WD$）指标进行评估，并开展问卷调查（$N=25$）评估生成摘要质量。TextSplit算法在两个评估指标上均取得最低均值（$\bar{P_k}=0.41$，$\bar{WD}=0.41$），而T5模型生成的摘要最优，其相关性得分仅比人工撰写标题低$8\%$。