Paper Info
Title
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
Abstract
The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). Essentially, the CLS task is the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT&MS and CLS. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize. To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder. The hierarchical model contains two kinds of latent variables at the local and global levels, respectively. At the local level, there are two latent variables, one for translation and the other for summarization. As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables. Experiments on two language directions (English.Chinese) verify the effectiveness and superiority of the proposed approach. In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the fewshot setting.
Year
DOI
Venue
2022
10.18653/v1/2022.acl-long.148
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS)
DocType
Volume
Citations 
Conference
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yunlong Liang122.79
Fandong Meng23119.11
Chulun Zhou332.17
Jin An Xu41524.50
Yufeng Chen53816.55
Jinsong Su626041.51
Jie Zhou72103190.17