Exploration of Podcast Corpora, Summarization, and Search
| dc.contributor.advisor | Khan, Latifur | |
| dc.creator | Perez, Mathew | |
| dc.date.accessioned | 2021-02-05T20:30:54Z | |
| dc.date.available | 2021-02-05T20:30:54Z | |
| dc.date.created | 2020-12 | |
| dc.date.issued | 2020-11-17 | |
| dc.date.submitted | December 2020 | |
| dc.date.updated | 2021-02-05T20:30:55Z | |
| dc.description.abstract | Podcasts have emerged as an increasingly ubiquitous form of media. This new medium carries several idiosyncrasies, such as multiple speakers, varying audio quality, oscillating topics, (etc.). As podcast consumption grows, so too does the need for knowledge and algorithms to apply to this burgeoning data space. We focus on two useful data tasks: summarization and search, developing methods to tackle both problems and discuss how existing methods in both areas can be tailored to podcast data. Specifically, we use Spotify’s podcast dataset, comprising episodes from their ever-growing database of podcasts, as a case study in the data space. Also, we explore this novel dataset, drawing several judgements and patterns regarding the nature of podcast data. Then, we conclude by considering future work and improvements as podcast data continues to grow and its analysis matures. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10735.1/9176 | |
| dc.language.iso | en | |
| dc.subject | Podcasts | |
| dc.subject | Search engines | |
| dc.subject | Corpora (Linguistics) | |
| dc.title | Exploration of Podcast Corpora, Summarization, and Search | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Computer Science | |
| thesis.degree.grantor | The University of Texas at Dallas | |
| thesis.degree.level | Masters | |
| thesis.degree.name | MSCS |
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