Spotify在TREC 2020：类型感知抽象播客总结
Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization
摘要：
本文描述了我们提交给2020年TREC（文本检索会议）播客轨道摘要任务的内容。这项挑战的目标是使用自动生成的播客音频转录来生成简短、信息丰富的摘要，其中包含播客片段中存在的关键信息。由于播客不同的风格，主题和粒度的信息，我们提出了两个摘要模型，明确考虑到体裁和命名实体，以生成适当的播客风格的摘要。我们的模型是抽象的，并使用创建者提供的描述作为基础事实摘要进行监督。提交的摘要的结果表明，我们最好的模型实现了1.58的总质量分数相比，创建者的描述和基线抽象系统，都得分1.49（9%的改进），由人类评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
