This document describes the Portuguese language podcast dataset released by
Spotify for academic research purposes. We give an overview of how the data was
sampled, some basic statistics over the collection, as well as brief
information of distribution over Brazilian and Portuguese dialects.

介绍了 Spotify 发布的用于学术研究目的的葡语语言播客数据集，概述了数据的采样方式、收集的基本统计数据以及巴西和葡萄牙方言的分布情况。

一千个播客：葡语口语文件语料库

Cem Mil Podcasts: A Spoken Portuguese Document Corpus

While there is an abundance of popular writing targeted to podcast creators
on how to speak in ways that engage their listeners, there has been little
data-driven analysis of podcasts that relates linguistic style with listener
engagement. In this paper, we investigate how various factors -- vocabulary
diversity, distinctiveness, emotion, and syntax, among others -- correlate with
engagement, based on analysis of the creators' written descriptions and
transcripts of the audio. We build models with different textual
representations, and show that the identified features are highly predictive of
engagement. Our analysis tests popular wisdom about stylistic elements in
high-engagement podcasts, corroborating some aspects, and adding new
perspectives on others.

本文通过分析创作者的文字描述和音频稿件的文字转录，研究了语言风格与听众情感关系的各种因素，如词汇多样性、独特性、情感和句法等，并构建了不同的文本表示模型以显示出这些特点对听众情感反应的高度预测性，得出结论：流行的播客风格是有效的，但也有新的风格视角。

播客中的语言使用建模及听众参与度分析

Modeling Language Usage and Listener Engagement in Podcasts

Podcast episodes often contain material extraneous to the main content, such
as advertisements, interleaved within the audio and the written descriptions.
We present classifiers that leverage both textual and listening patterns in
order to detect such content in podcast descriptions and audio transcripts. We
demonstrate that our models are effective by evaluating them on the downstream
task of podcast summarization and show that we can substantively improve ROUGE
scores and reduce the extraneous content generated in the summaries.

本研究提出一种旨在检测广告等信息的分类器，通过分析播客描述和音频文本的文本和听取模式，在播客节目摘要制作中有效地减少了额外生成的内容，从而实现了对播客节目的自动化摘要制作。

检测播客中的无关内容

Detecting Extraneous Content in Podcasts

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 mainly built on fine-tuned models on
professionally edited texts such as CNN and DailyMail news. Different from
news, podcasts are often longer, more colloquial and conversational, and
noisier with contents on commercials and sponsorship, which makes automatic
podcast summarization extremely challenging. This paper presents a baseline
analysis of podcast summarization using the Spotify Podcast Dataset provided by
TREC 2020. It aims to help researchers understand current state-of-the-art
pre-trained models and hence build a foundation for creating better models.

使用 Spotify Podcast 数据集对 Podcast 的 summarization 进行基础分析，以帮助研究者了解现有的预训练模型并构建更好的模型。