Cross-Lingual Abstractive Sentence Summarization(CL-ASSUM)
Introduction
We implemented CL-ASSUM on fairseq. In this repo, it contains of four parts.
- Transformer
- Teaching-Generation
- Teaching-Attention
- Teaching-Generation-Attention
Teacher models
Before staring the experiment, you should first use Transformer to train the teacher model of NMT model and momolingual summarization model.
Please refer to Transformer for more deatils.
Teaching-Generation
Teaching-Attention
Teaching-Generation-Attention
Evaluation sets of CL-ASSUM
The test-data file contains evaluation sets of CL-ASSUM, which is built by manual translation.
Requirements and Installation
- A PyTorch installation
- For training new models, you'll also need an NVIDIA GPU and NCCL
- Python version 3.6
- PyTorch version >= 0.4.0.
Cross-Lingual Test Set
In our experiments, we manually translate the English sentences into the Chinese sentences for the validation and evaluation sets of Gigaword and DUC2004.
License
Reference
If you find CL-ASSUM useful in your work, you can cite this paper as below:
@inproceedings{duan-etal-2019-zero,
title = "Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention",
author = "Duan, Xiangyu and Yin, Mingming and Zhang, Min and Chen, Boxing and Luo, Weihua",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1305",
doi = "10.18653/v1/P19-1305",
pages = "3162--3172",
}