# Multi-Task Learning for Cross-Lingual Abstractive Summarization

arXiv: https://arxiv.org/abs/2010.07503 · HTML: https://mlsanity.com/paper/2010.07503/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [Unifying Cross-lingual Summarization and Machine Translation with Compression Rate](https://mlsanity.com/paper/2110.07936/) (2021)
- [The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation](https://mlsanity.com/paper/2305.09652/) (2023)
- [ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2203.02797/) (2022)
- [SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism](https://mlsanity.com/paper/2304.01621/) (2023)
- [ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents](https://mlsanity.com/paper/2408.09273/) (2024)

## Abstract (arXiv)

We present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce existing genuine data such as translation pairs and monolingual abstractive summarization data into training. Our proposed method, Transum, attaches a special token to the beginning of the input sentence to indicate the target task. The special token enables us to incorporate the genuine data into the training data easily. The experimental results show that Transum achieves better performance than the model trained with only pseudo cross-lingual summarization data. In addition, we achieve the top ROUGE score on Chinese-English and Arabic-English abstractive summarization. Moreover, Transum also has a positive effect on machine translation. Experimental results indicate that Transum improves the performance from the strong baseline, Transformer, in Chinese-English, Arabic-English, and English-Japanese translation datasets.