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dc.contributor.author | Malykh V. | |
dc.contributor.author | Porplenko D. | |
dc.contributor.author | Tutubalina E. | |
dc.date.accessioned | 2022-02-09T20:33:42Z | |
dc.date.available | 2022-02-09T20:33:42Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/169023 | |
dc.description.abstract | We present a novel dataset of sports broadcasts with 8,781 games. The dataset contains 700 thousand comments and 93 thousand related news documents in Russian. We run an extensive series of experiments of modern extractive and abstractive approaches. The results demonstrate that BERT-based models show modest performance, reaching up to 0.26 ROUGE-1F-measure. In addition, human evaluation shows that neural approaches could generate feasible although inaccurate news basing on broadcast text. | |
dc.relation.ispartofseries | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Neural networks | |
dc.subject | Russian language | |
dc.subject | Sport broadcast | |
dc.subject | Summarization | |
dc.title | Generating Sport Summaries: A Case Study for Russian | |
dc.type | Conference Proceeding | |
dc.relation.ispartofseries-volume | 12602 LNCS | |
dc.collection | Публикации сотрудников КФУ | |
dc.relation.startpage | 149 | |
dc.source.id | SCOPUS03029743-2021-12602-SID85104723895 |