# Unifying Cross-lingual Summarization and Machine Translation with Compression Rate

arXiv: https://arxiv.org/abs/2110.07936 · HTML: https://mlsanity.com/paper/2110.07936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [NCLS: Neural Cross-Lingual Summarization](https://mlsanity.com/paper/1909.00156/) (2019)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://mlsanity.com/paper/2105.13648/) (2021)
- [Mixed-Lingual Pre-training for Cross-lingual Summarization](https://mlsanity.com/paper/2010.08892/) (2020)
- [Multi-Task Learning for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.07503/) (2020)

## Built on by (most cited first, 5 of 5)

- [SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization](https://mlsanity.com/paper/2505.11166/) (2025)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism](https://mlsanity.com/paper/2304.01621/) (2023)
- [Evaluating the Efficacy of Length-Controllable Machine Translation](https://mlsanity.com/paper/2305.02300/) (2023)
- [An Empirical Study of Many-to-Many Summarization with Large Language Models](https://mlsanity.com/paper/2505.12983/) (2025)

## Abstract (arXiv)

Cross-Lingual Summarization (CLS) is a task that extracts important information from a source document and summarizes it into a summary in another language. It is a challenging task that requires a system to understand, summarize, and translate at the same time, making it highly related to Monolingual Summarization (MS) and Machine Translation (MT). In practice, the training resources for Machine Translation are far more than that for cross-lingual and monolingual summarization. Thus incorporating the Machine Translation corpus into CLS would be beneficial for its performance. However, the present work only leverages a simple multi-task framework to bring Machine Translation in, lacking deeper exploration. In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit Cross-Lingual Summarization by large-scale Machine Translation corpus. Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%. Hence they can be trained as a unified task, sharing knowledge more effectively. However, a huge gap exists between the MT task and the CLS task, where samples with compression rates between 30% and 90% are extremely rare. Hence, to bridge these two tasks smoothly, we propose an effective data augmentation method to produce document-summary pairs with different compression rates. The proposed method not only improves the performance of the CLS task, but also provides controllability to generate summaries in desired lengths. Experiments demonstrate that our method outperforms various strong baselines in three cross-lingual summarization datasets. We released our code and data at https://github.com/ybai-nlp/CLS_CR.