NCLS: Neural Cross-Lingual Summarization

By Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Chengqing Zong. 2019

145 citations recorded by the source corpus.

Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. Existing methods simply divide this task into two steps: summarization and translation, leading to the problem of error propagation. To handle that, we present an end-to-end CLS framework, which we refer to as Neural Cross-Lingual Summarization (NCLS), for the first time. Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning. Due to the lack of supervised CLS data, we propose a round-trip translation strategy to acquire two high-quality large-scale CLS datasets based on existing monolingual summarization datasets. Experimental results have shown that our NCLS achieves remarkable improvement over traditional pipeline methods on both English-to-Chinese and Chinese-to-English CLS human-corrected test sets. In addition, NCLS with multi-task learning can further significantly improve the quality of generated summaries. We make our dataset and code publicly available here: http://www.nlpr.ia.ac.cn/cip/dataset.htm.

Subfields: Artificial Intelligence, General Social Sciences, Natural Language Processing

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Citations in the corpus

WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization (2020, background): “Zhu et al. (2019) use round-trip translation of large scale monolingual datasets (Hermann et al., 2015; Zhu et al., 2018; Hu et al., 2015) to generate synthetic training data for their models, and train a multi-task model to to learn both translation and cross-lingual summarization.”
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning (2020, background): “…et al., 2014; Zhang et al., 2016; Kim et al., 2017), named entity recognition (Ni et al., 2017; Xie et al., 2018), abstractive summarization (Duan et al., 2019; Zhu et al., 2019), dependency parsing (Schuster et al., 2019; Ahmad et al., 2019), and personalized dialogue agents (Lin et al., 2020).”
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding (2020, background): “…multilingual datasets range over tasks such as document classiﬁcation, natural language inference, information retrieval, paraphrase identiﬁcation, and summarization (Klementiev, Titov, and Bhattarai 2012; Cer et al. 2017; Conneau et al. 2018; Sasaki et al. 2018; Yang et al. 2019; Zhu et al. 2019).”
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021, methodology): “In future, we will investigate the use of our dataset for other summarization tasks (e.g., cross-lingual summarization Zhu et al., 2019).”
MTG: A Benchmark Suite for Multilingual Text Generation (2021, background): “Others involve cross-lingual pairs while English is included on either source or target side in most cases (Zhu et al., 2019; Ladhak et al., 2020), leading to difficult transfer between low-resource or distant language pairs.”
Models and Datasets for Cross-Lingual Summarisation (2022, methodology): “For this reason, existing approaches either create large-scale synthetic data using back-translation (Zhu et al., 2019; Cao et al., 2020), translate the input documents (Ouyang et al., 2019), or build document-summary pairs from social media annotations and crowd-sourcing (Nguyen and Daumé III,…”