Generating Sport Summaries: A Case Study for Russian

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

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Публикации сотрудников КФУ Scopus [24551]
Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.