Edwards, Gareth RSefara, Tshephisho J2024-06-112024-06-112024-04Edwards, G.R. & Sefara, T.J. 2024. Text summarisation for low-resourced languages: A review. http://hdl.handle.net/10204/13690 .978-3-031-58494-7978-3-031-58495-4https://doi.org/10.1007/978-3-031-58495-4_21http://hdl.handle.net/10204/13690Text summarisation is becoming increasingly important for humans to more quickly understand and analyse documents with large amounts of text. In this paper, we review and discuss approaches and methods used in the development of text summarisation models for low-resourced languages, specifically South African languages. We compare approaches and results to give guidance on what may be the best approach to building a sophisticated text summarisation model for South African languages. The results showed that there is one text summarisation model created for isiXhosa out of 11 South African languages, and only a few studies were done for African languages.FulltextenText summarisationLow-resource languagesNatural Language ProcessingConference PresentationEdwards, G. R., & Sefara, T. J. (2024). Text summarisation for low-resourced languages: A review. http://hdl.handle.net/10204/13690Edwards, Gareth R, and Tshephisho J Sefara. "Text summarisation for low-resourced languages: A review." <i>Second International Conference, SPELLL 2023, Erode, India, 6-8 December 2023</i> (2024): http://hdl.handle.net/10204/13690Edwards GR, Sefara TJ, Text summarisation for low-resourced languages: A review; 2024. http://hdl.handle.net/10204/13690 .TY - Conference Presentation AU - Edwards, Gareth R AU - Sefara, Tshephisho J AB - Text summarisation is becoming increasingly important for humans to more quickly understand and analyse documents with large amounts of text. In this paper, we review and discuss approaches and methods used in the development of text summarisation models for low-resourced languages, specifically South African languages. We compare approaches and results to give guidance on what may be the best approach to building a sophisticated text summarisation model for South African languages. The results showed that there is one text summarisation model created for isiXhosa out of 11 South African languages, and only a few studies were done for African languages. DA - 2024-04 DB - ResearchSpace DP - CSIR J1 - Second International Conference, SPELLL 2023, Erode, India, 6-8 December 2023 KW - Text summarisation KW - Low-resource languages KW - Natural Language Processing LK - https://researchspace.csir.co.za PY - 2024 SM - 978-3-031-58494-7 SM - 978-3-031-58495-4 T1 - Text summarisation for low-resourced languages: A review TI - Text summarisation for low-resourced languages: A review UR - http://hdl.handle.net/10204/13690 ER -27842