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arXiv · 2608.10359

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation

Yejin Jeon·Marie Maltais·Virginia Ceccatelli·Min Ma·David Ifeoluwa Adelani
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Abstract

As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Nevertheless, long-document summarization research remains text-centric, whereas multilingual speech research has largely prioritized translation, preserving source content rather than compressing it. We address this methodological gap by formalizing joint speech summarization and translation (JSumT): the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language. We additionally introduce VoxSumm, the first multilingual and cross-lingual benchmark for this task, comprising 10,045 BBC article-summary pairs across 24 languages and encompassing approximately 703 hours of speech data. Our evaluation of representative speech-language models reveals pronounced variation across models and generation settings: Gemini3.1-Pro demonstrates the greatest consistency, summarization into English generally surpasses generation into non-English target languages, and translating an entire document before summarization compounds instruction-following failures. Through the release of VoxSumm, we establish a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.

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Yejin Jeon, Marie Maltais, Virginia Ceccatelli, Min Ma, David Ifeoluwa Adelani. 2026-08-11. VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation. https://arxiv.org/abs/2608.10359

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