@inproceedings{palen-michel-lignos-2026-comparing,
title = "Comparing Approaches to Automatic Summarization in Less-Resourced Languages",
author = "Palen-Michel, Chester and
Lignos, Constantine",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.270/",
doi = "10.63317/2pi7c62tdqsr",
pages = "3402--3422",
abstract = "Automatic text summarization has achieved high performance in higher-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance at similar model sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be unreliable on less-resourced languages."
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<abstract>Automatic text summarization has achieved high performance in higher-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance at similar model sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be unreliable on less-resourced languages.</abstract>
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%0 Conference Proceedings
%T Comparing Approaches to Automatic Summarization in Less-Resourced Languages
%A Palen-Michel, Chester
%A Lignos, Constantine
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F palen-michel-lignos-2026-comparing
%X Automatic text summarization has achieved high performance in higher-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance at similar model sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be unreliable on less-resourced languages.
%R 10.63317/2pi7c62tdqsr
%U https://aclanthology.org/2026.lrec-1.270/
%U https://doi.org/10.63317/2pi7c62tdqsr
%P 3402-3422
Markdown (Informal)
[Comparing Approaches to Automatic Summarization in Less-Resourced Languages](https://aclanthology.org/2026.lrec-1.270/) (Palen-Michel & Lignos, LREC 2026)
ACL