With podcasts being a fast growing audio-only form of media, an effective way of promoting different podcast shows becomes more and more vital to all the stakeholders concerned, including the podcast creators, the podcast streaming platforms, and the podcast listeners. This thesis investigates the relatively little studied topic of automatic podcast trailer generation, with the purpose of en- hancing the overall visibility and publicity of different podcast contents and gen- erating more user engagement in podcast listening. This thesis takes a hotspot- based approach, by specifically defining the vague concept of “hotspot” and designing different appropriate methods for hotspot detection. Different meth- ods are analyzed and compared, and the best methods are selected. The selected methods are then used to construct an automatic podcast trailer generation sys- tem, which consists of four major components and one schema to coordinate the components. The system can take a random podcast episode audio as input and generate an around 1 minute long trailer for it. This thesis also proposes two human-based podcast trailer evaluation approaches, and the evaluation results show that the proposed system outperforms the baseline with a large margin and achieves promising results in terms of both aesthetics and functionality.