# Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach

arXiv: https://arxiv.org/abs/2410.00502 · HTML: https://mlsanity.com/paper/2410.00502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [SONAR: Sentence-Level Multimodal and Language-Agnostic Representations](https://mlsanity.com/paper/2308.11466/) (2023)
- [NCLS: Neural Cross-Lingual Summarization](https://mlsanity.com/paper/1909.00156/) (2019)
- [OpenKiwi: An Open Source Framework for Quality Estimation](https://mlsanity.com/paper/1902.08646/) (2019)
- [CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs](https://mlsanity.com/paper/2112.08804/) (2021)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [Models and Datasets for Cross-Lingual Summarisation](https://mlsanity.com/paper/2202.09583/) (2022)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://mlsanity.com/paper/2105.13648/) (2021)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric](https://mlsanity.com/paper/2212.08486/) (2022)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task](https://mlsanity.com/paper/1907.10352/) (2019)
- [PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India](https://mlsanity.com/paper/2305.08828/) (2023)
- [μPLAN: Summarizing using a Content Plan as Cross-Lingual Bridge](https://mlsanity.com/paper/2305.14205/) (2023)
- [Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation](https://mlsanity.com/paper/2307.04018/) (2023)

## Built on by (most cited first, 2 of 2)

- [MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks](https://mlsanity.com/paper/2507.19634/) (2025)
- [Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization](https://mlsanity.com/paper/2606.01252/) (2026)

## Abstract (arXiv)

Cross-lingual summarization aims to bridge language barriers by summarizing documents in different languages. However, ensuring semantic coherence across languages is an overlooked challenge and can be critical in several contexts. To fill this gap, we introduce multi-target cross-lingual summarization as the task of summarizing a document into multiple target languages while ensuring that the produced summaries are semantically similar. We propose a principled re-ranking approach to this problem and a multi-criteria evaluation protocol to assess semantic coherence across target languages, marking a first step that will hopefully stimulate further research on this problem.