Socially-Motivated Music Recommendation
Ben Lacker, Samuel Way
Abstractive summarization of podcasts is motivated by the growing popularity of podcasts and the needs of their listeners. Podcasting is a markedly different domain from news and other media that are commonly studied in the context of automatic summarization. As such, the qualities of a good podcast summary are yet unknown. Using a collection of podcast summaries produced by different algorithms alongside human judgments of summary quality obtained from the TREC 2020 Podcasts Track, we study the correlations between various automatic evaluation metrics and human judgments, as well as the linguistic aspects of summaries that result in strong evaluations.
Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard
Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou