Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization

التفاصيل البيبلوغرافية
العنوان: Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization
المؤلفون: Rezapour, Rezvaneh, Reddy, Sravana, Clifton, Ann, Jones, Rosie
سنة النشر: 2021
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language
الوصف: 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.
Comment: The Twenty-Ninth Text REtrieval Conference (TREC 2020) Proceedings
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2104.03343
رقم الأكسشن: edsarx.2104.03343
قاعدة البيانات: arXiv