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        <title>Translation-Augmented Multilingual Summarization for Low-Resource Languages</title>
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            <title>Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion</title>
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        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="given">Bharathi</namePart>
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        <name type="personal">
            <namePart type="given">Paul</namePart>
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            <namePart type="given">Durairaj</namePart>
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        <name type="personal">
            <namePart type="given">Miguel</namePart>
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            <namePart type="family">García Cumbreras</namePart>
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            <namePart type="given">Salud</namePart>
            <namePart type="given">María</namePart>
            <namePart type="family">Jiménez Zafra</namePart>
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    <abstract>While automatic text summarization has achieved remarkable success in English,extending these capabilities to low-resource languages remains a significantchallenge due to the scarcity of labeled training data. We propose atranslation-augmented approach to multilingual summarization: we systematicallytranslate high-quality English summarization corpora into low-resource targetlanguages using NLLB-200, and use the resulting parallel data to train andevaluate sequence-to-sequence models. We experiment across three typologicallydiverse languages—Swahili, Hausa, and Afrikaans—comparing monolingualfine-tuning (MONO), cross-lingual transfer (XLT), and joint multilingualtraining (TAMT) on mBART-large-50. Monolingual fine-tuning achieves the bestperformance for Swahili (ROUGE-L 13.9) and Afrikaans (ROUGE-L 15.7),surpassing the Lead-3 baseline in both cases, while cross-lingual transferremains strongest for Hausa (ROUGE-L 14.5). We show that native language tokenavailability in mBART-50 is a critical determinant of fine-tuning performance,and characterize the conditions under which the theoretically expectedTAMT &gt; MONO &gt; XLT ordering breaks down. We release our dataset, code, andevaluation infrastructure to support future research on low-resourcemultilingual summarization.</abstract>
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%0 Conference Proceedings
%T Translation-Augmented Multilingual Summarization for Low-Resource Languages
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%A Prasanth
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F prasanth-2026-translation
%X While automatic text summarization has achieved remarkable success in English,extending these capabilities to low-resource languages remains a significantchallenge due to the scarcity of labeled training data. We propose atranslation-augmented approach to multilingual summarization: we systematicallytranslate high-quality English summarization corpora into low-resource targetlanguages using NLLB-200, and use the resulting parallel data to train andevaluate sequence-to-sequence models. We experiment across three typologicallydiverse languages—Swahili, Hausa, and Afrikaans—comparing monolingualfine-tuning (MONO), cross-lingual transfer (XLT), and joint multilingualtraining (TAMT) on mBART-large-50. Monolingual fine-tuning achieves the bestperformance for Swahili (ROUGE-L 13.9) and Afrikaans (ROUGE-L 15.7),surpassing the Lead-3 baseline in both cases, while cross-lingual transferremains strongest for Hausa (ROUGE-L 14.5). We show that native language tokenavailability in mBART-50 is a critical determinant of fine-tuning performance,and characterize the conditions under which the theoretically expectedTAMT > MONO > XLT ordering breaks down. We release our dataset, code, andevaluation infrastructure to support future research on low-resourcemultilingual summarization.
%U https://aclanthology.org/2026.ltedi-1.10/
%P 108-117

Markdown (Informal)

[Translation-Augmented Multilingual Summarization for Low-Resource Languages](https://aclanthology.org/2026.ltedi-1.10/) (Prasanth, LTEDI 2026)

• Translation-Augmented Multilingual Summarization for Low-Resource Languages (Prasanth, LTEDI 2026)

ACL

• Prasanth. 2026. Translation-Augmented Multilingual Summarization for Low-Resource Languages. In Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 108–117, Virtual (Online). Association for Computational Linguistics.