Zero-Shot Cross-Lingual Neural Headline Generation

By Ayana, Shiqi Shen, Yun Chen, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2018

54 citations recorded by the source corpus.

Neural headline generation (NHG) has been proven to be effective in generating a fully abstractive headline recently. Existing NHG systems are only capable of producing headline of the same language as the original document. Cross lingual headline generation is an important task since it provides an efficient way to understand the key point of a document in a different language. Due to the lack of those parallel corpora of direct source language articles and target language headlines, we propose to deal with the cross-lingual neural headline generation (CNHG) under the zero-shot scenario. A trivial solution is to translate and summarize the source document in a pipeline way. However, a pipeline solution will lead to error propagation in the translation and summarization phases. This challenge motivates us to build a direct source-to-target CNHG model based on existing parallel corpora of translation and monolingual headline generation. Specifically, we let a parameterized CNHG model (student model) mimic the output of a pretrained translation or headline generation model (teacher model). To the best of our knowledge, this is the first effort to address CNHG problem. Besides, we construct English–Chinese headline generation evaluation datasets by manual translation. Experimental results on English-to-Chinese cross-lingual headline generation demonstrate that our proposed method significantly outperforms the baseline models.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Furu Wei
2. Li Dong
3. Mohit Bansal
4. Wenhui Wang
5. Claire Cardie
6. Jianfeng Gao
7. Zewen Chi
8. Mirella Lapata

In Natural Language Processing

1. Furu Wei
2. Li Dong
3. Wenhui Wang
4. Mohit Bansal
5. Faisal Ladhak
6. K. McKeown
7. Claire Cardie
8. Esin Durmus

Citations in the corpus

• WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization (2020, background): “Shen et al. (2018) propose zero-shot cross-lingual headline generation to generate Chinese headlines for English articles, via a teacher-student framework, using two teacher models.”
• Cross-Lingual Natural Language Generation via Pre-Training (2019, background): “Another strand of work uses MT to generate pseudo training data for other language pairs that are lack of annotations (Shen et al. 2018; Duan et al. 2019).”
• Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (2019, methodology): “When comparing the student networks, we can find that we adopt pseudo source sentence, while Ayana et al. (2018) adopt pseudo target summary.”
• NCLS: Neural Cross-Lingual Summarization (2019, background): “Recently, Ayana et al. (2018) present zero-shot cross-lingual headline generation based on existing parallel corpora of translation and monolingual headline generation.”