AfriHG: News Headline Generation for African Languages

Toyib Ogunremi, Serah sessi Akojenu, Anthony Soronnadi, Olubayo Adekanmbi, David Ifeoluwa Adelani

Keywords: Abstractive Summarization, T5 models, News Headline Generation, African Languages

TL;DR: We fine-tune two T5-like seq2seq models on abstractive summarization task for low-resource languages.

Abstract: This paper introduces AfriHG, an extended multi-lingual corpus compiled from XL-Sum and Masakhanews focusing on 16 languages widely spoken by Africans across 9 language families. We experimented with two seq-2-seq models. We also evaluated our dataset with a massively multilingual instruction-tuned LLM and benchmarked our results in the domain of abstractive summarization for News headline generation.

Submission Number: 30