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However, they often rely on the topical knowledge of the listener in order for them to be fully understood. We describe an investigation into methods to augment the contents of podcasts with related information from the Web. We seek to identify webpages related to segments within a podcast. NLP techniques are used to analyze audio podcast transcripts and link these to related content. We propose and examine 10 methods for automatically generating search queries from transcript segments, which are then used to search for related content on the web. The relevance of retrieved webpages to retrieved content is evaluated using crowdsourcing via Amazon Mechanical Turk. Extracting key phrases directly from the podcasts using YAKE was the most successful approach with more than 90% returned pages assessed as relevant, with precision at rank 1 and rank 3 above 0.9.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_30","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"381-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Linking of\u00a0Podcast Segments to\u00a0Topically Related Webpages"],"prefix":"10.1007","author":[{"given":"Carla","family":"McKeon","sequence":"first","affiliation":[]},{"given":"Claudio","family":"Rocha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2923-8365","authenticated-orcid":false,"given":"Gareth J. 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