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This work explores the algorithms that were the product of these advances, focusing on the domain of sports news summarisation. By creating a new hybrid evaluation system that incorporates automatic evaluation metrics, such as ROUGE and BLEU scores, with human evaluation, we observe that abstractive techniques return the best results in the sports domain. This also generalises to the domain of political articles. However, here the metrics report lower scores across most algorithms. Another finding is that the algorithms considered perform independently of the dialect of English used.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_29","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"368-380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Abstractive vs. Extractive Summarisation Techniques for\u00a0Sports News"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0465-0265","authenticated-orcid":false,"given":"Ahmed","family":"Jouda","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Saggion, H., Poibeau, T.: Multi-source, Multilingual Information Extraction and Summarization, pp. 3\u201313. 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