AfriHG: News headline generation for African Languages
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Abstract (arXiv)
This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 languages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multilingual mT5-base model. Finally, we show that the performance of fine-tuning AfriTeVa V2 with 313M parameters is competitive to prompting Aya-101 LLM with more than 13B parameters.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 28 Dec 2024.
Builds on
4 of 12 references.
References · 4
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
- 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 347
- 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.