Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention

Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo


Abstract
Abstractive Sentence Summarization (ASSUM) targets at grasping the core idea of the source sentence and presenting it as the summary. It is extensively studied using statistical models or neural models based on the large-scale monolingual source-summary parallel corpus. But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system. We propose to solve this zero-shot problem by using resource-rich monolingual ASSUM system to teach zero-shot cross-lingual ASSUM system on both summary word generation and attention. This teaching process is along with a back-translation process which simulates source-summary pairs. Experiments on cross-lingual ASSUM task show that our proposed method is significantly better than pipeline baselines and previous works, and greatly enhances the cross-lingual performances closer to the monolingual performances.
Anthology ID:
P19-1305
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3162–3172
Language:
URL:
https://aclanthology.org/P19-1305
DOI:
10.18653/v1/P19-1305
Bibkey:
Cite (ACL):
Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, and Weihua Luo. 2019. Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3162–3172, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (Duan et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1305.pdf
Code
 KelleyYin/Cross-lingual-Summarization
Data
LCSTS