SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization

Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi


Abstract
Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest. However, the scarcity of fine-tuning samples makes this approach challenging in some cases. For this reason, in this paper we propose revisiting the summarize-and-translate pipeline, where the summarization and translation tasks are performed in a sequence. This approach allows reusing the many, publicly-available resources for monolingual summarization and translation, obtaining a very competitive zero-shot performance. In addition, the proposed pipeline is completely differentiable end-to-end, allowing it to take advantage of few-shot fine-tuning, where available. Experiments over two contemporary and widely adopted XLS datasets (CrossSum and WikiLingua) have shown the remarkable zero-shot performance of the proposed approach, and also its strong few-shot performance compared to an equivalent multilingual LM baseline, that the proposed approach has been able to outperform in many languages with only 10% of the fine-tuning samples.
Anthology ID:
2024.naacl-long.133
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2399–2415
Language:
URL:
https://aclanthology.org/2024.naacl-long.133
DOI:
Bibkey:
Cite (ACL):
Jacob Parnell, Inigo Jauregi Unanue, and Massimo Piccardi. 2024. SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2399–2415, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization (Parnell et al., NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-long.133.pdf
Copyright:
 2024.naacl-long.133.copyright.pdf