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Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization

arXiv 2104.03343 ↗First posted 7 Apr 2021cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 7 Apr 2021.

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References · 11

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  11. 2021Detecting Extraneous Content in Podcastscited by 7

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