Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531802 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
podcast summarization, abstractive text summarization, evaluation, ROUGE | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rezvaneh Rezapour | 1 | 7 | 5.19 |
| Sravana Reddy | 2 | 0 | 0.34 |
| Rosie Jones | 3 | 0 | 0.34 |
| Ian Soboroff | 4 | 1907 | 218.39 |