# NCLS: Neural Cross-Lingual Summarization

arXiv: https://arxiv.org/abs/1909.00156 · HTML: https://mlsanity.com/paper/1909.00156/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 30 of 64)

- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [A Survey on Large Language Model Acceleration based on KV Cache Management](https://mlsanity.com/paper/2412.19442/) (2024)
- [Summarization is (Almost) Dead](https://mlsanity.com/paper/2309.09558/) (2023)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [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)
- [Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://mlsanity.com/paper/2105.13648/) (2021)
- [CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization](https://mlsanity.com/paper/2108.13139/) (2021)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [MTG: A Benchmark Suite for Multilingual Text Generation](https://mlsanity.com/paper/2108.07140/) (2021)
- [Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation](https://mlsanity.com/paper/2112.03473/) (2021)
- [WeLM: A Well-Read Pre-trained Language Model for Chinese](https://mlsanity.com/paper/2209.10372/) (2022)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval](https://mlsanity.com/paper/2204.02292/) (2022)
- [CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark](https://mlsanity.com/paper/2112.13610/) (2021)
- [Zero-Shot Cross-Lingual Summarization via Large Language Models](https://mlsanity.com/paper/2302.14229/) (2023)
- [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2310.19240/) (2023)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://mlsanity.com/paper/2205.15051/) (2022)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India](https://mlsanity.com/paper/2305.08828/) (2023)
- [XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags](https://mlsanity.com/paper/2406.03776/) (2024)
- [GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization](https://mlsanity.com/paper/2410.04087/) (2024)
- [The Cross-lingual Conversation Summarization Challenge](https://mlsanity.com/paper/2205.00379/) (2022)
- [CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization](https://mlsanity.com/paper/2303.04092/) (2023)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)

## Abstract (arXiv)

Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. Existing methods simply divide this task into two steps: summarization and translation, leading to the problem of error propagation. To handle that, we present an end-to-end CLS framework, which we refer to as Neural Cross-Lingual Summarization (NCLS), for the first time. Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning. Due to the lack of supervised CLS data, we propose a round-trip translation strategy to acquire two high-quality large-scale CLS datasets based on existing monolingual summarization datasets. Experimental results have shown that our NCLS achieves remarkable improvement over traditional pipeline methods on both English-to-Chinese and Chinese-to-English CLS human-corrected test sets. In addition, NCLS with multi-task learning can further significantly improve the quality of generated summaries. We make our dataset and code publicly available here: http://www.nlpr.ia.ac.cn/cip/dataset.htm.