Abstract
The Chinese-Vietnamese bilingual news topic representations are generated from Chinese-Vietnamese bilingual news texts describing the same topic into concise Chinese sentences that can correctly describe the topic. However, there is a semantic gap between Chinese and Vietnamese, and the association relationship between multiple documents in multiple languages is complicated, which makes it challenging to generate concise and correct topic representations. In this paper, we propose a cross-language topic representation method based on heterogeneous graphs. The method first uses a heterogeneous graph containing sentences and entity nodes to represent bilingual Chinese-Vietnamese news texts and effectively models the complex association relationships between multiple texts in multiple languages through graph attention networks (GAT). The topic encoder is then used to encode topic words into cues for topic representation generation, and the decoder side constraints are incorporated to generate the correct topic representation. The experimental results show that the proposed method improves the ROUGE value by up to 3.5 compared with the baseline method.
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Acknowledgements
The research work described in this paper has been supported by the National Natural Science Foundation of China (U21B2027, 61972186, 62266028), Yunnan provincial major science and technology special plan projects (202002AD080001, 202103AA080015, 202202AD080003), Yunnan High and New Technology Industry Project (201606). We thank the three anonymous reviewers for their insightful comments and suggestions.
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He, Z., Zhu, E., Yu, Z., Gao, S., Huang, Y., Xia, L. (2023). Representation of Chinese-Vietnamese Bilingual News Topics Based on Heterogeneous Graph. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_19
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