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This paper contains the description of our submissions to the summarization task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The goal of this challenge was to generate short, informative summaries that contain the key information present in a podcast episode using automatically generated transcripts of the podcast audio. Since podcasts vary with respect to their genre, topic, and granularity of information, we propose two summarization models that explicitly take genre and named entities into consideration in order to generate summaries appropriate to the style of the podcasts. Our models are abstractive, and supervised using creator-provided descriptions as ground truth summaries. The results of the submitted summaries show that our best model achieves an aggregate quality score of 1.58 in comparison to the creator descriptions and a baseline abstractive system which both score 1.49 (an improvement of 9%) as assessed by human evaluators.
Podcast summary, an important factor affecting end-users listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications. Existing abstractive summarization approaches are m
Podcast summarization is different from summarization of other data formats, such as news, patents, and scientific papers in that podcasts are often longer, conversational, colloquial, and full of sponsorship and advertising information, which impose
Podcasts are a relatively new form of audio media. Episodes appear on a regular cadence, and come in many different formats and levels of formality. They can be formal news journalism or conversational chat; fiction or non-fiction. They are rapidly g
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progressio
Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-huma