# A Survey on Cross-Lingual Summarization

arXiv: https://arxiv.org/abs/2203.12515 · HTML: https://mlsanity.com/paper/2203.12515/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 96 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training](https://mlsanity.com/paper/2007.07834/) (2020)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Does Knowledge Distillation Really Work?](https://mlsanity.com/paper/2106.05945/) (2021)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [How2: A Large-scale Dataset for Multimodal Language Understanding](https://mlsanity.com/paper/1811.00347/) (2018)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)

## Built on by (most cited first, 30 of 38)

- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [Zero-Shot Cross-Lingual Summarization via Large Language Models](https://mlsanity.com/paper/2302.14229/) (2023)
- [UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization](https://mlsanity.com/paper/2211.09783/) (2022)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization](https://mlsanity.com/paper/2212.07672/) (2022)
- [MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks](https://mlsanity.com/paper/2507.19634/) (2025)
- [PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India](https://mlsanity.com/paper/2305.08828/) (2023)
- [Cross-Lingual Knowledge Editing in Large Language Models](https://mlsanity.com/paper/2309.08952/) (2023)
- [XRAG: Cross-lingual Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.10089/) (2025)
- [CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization](https://mlsanity.com/paper/2303.04092/) (2023)
- [Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review](https://mlsanity.com/paper/2505.04531/) (2025)
- [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)
- [Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2310.06365/) (2023)
- [Long-Document Cross-Lingual Summarization](https://mlsanity.com/paper/2212.00586/) (2022)
- [AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://mlsanity.com/paper/2308.04992/) (2023)
- [GOAL: Towards Benchmarking Few-Shot Sports Game Summarization](https://mlsanity.com/paper/2207.08635/) (2022)
- [Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment](https://mlsanity.com/paper/2210.04141/) (2022)
- [D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization](https://mlsanity.com/paper/2305.12767/) (2023)
- [Low Resource Summarization using Pre-trained Language Models](https://mlsanity.com/paper/2310.02790/) (2023)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach](https://mlsanity.com/paper/2410.00502/) (2024)
- [Towards Zero-Shot Functional Compositionality of Language Models](https://mlsanity.com/paper/2303.03103/) (2023)
- [Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model](https://mlsanity.com/paper/2306.10241/) (2023)
- [SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization](https://mlsanity.com/paper/2403.13240/) (2024)
- [Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets](https://mlsanity.com/paper/2509.24080/) (2025)

## Abstract (arXiv)

Cross-lingual summarization is the task of generating a summary in one language (e.g., English) for the given document(s) in a different language (e.g., Chinese). Under the globalization background, this task has attracted increasing attention of the computational linguistics community. Nevertheless, there still remains a lack of comprehensive review for this task. Therefore, we present the first systematic critical review on the datasets, approaches, and challenges in this field. Specifically, we carefully organize existing datasets and approaches according to different construction methods and solution paradigms, respectively. For each type of datasets or approaches, we thoroughly introduce and summarize previous efforts and further compare them with each other to provide deeper analyses. In the end, we also discuss promising directions and offer our thoughts to facilitate future research. This survey is for both beginners and experts in cross-lingual summarization, and we hope it will serve as a starting point as well as a source of new ideas for researchers and engineers interested in this area.