Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Summarizing the Differences in Chinese-Vietnamese Bilingual News

  • Wu, Jinjuan (Dept. of Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Yu, Zhengtao (Dept. of Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Liu, Shulong (Dept. of Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Zhang, Yafei (Dept. of Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Gao, Shengxiang (Dept. of Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology)
  • Received : 2017.11.06
  • Accepted : 2018.07.29
  • Published : 2019.12.31
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Abstract

Summarizing the differences in Chinese-Vietnamese bilingual news plays an important supporting role in the comparative analysis of news views between China and Vietnam. Aiming at cross-language problems in the analysis of the differences between Chinese and Vietnamese bilingual news, we propose a new method of summarizing the differences based on an undirected graph model. The method extracts elements to represent the sentences, and builds a bridge between different languages based on Wikipedia's multilingual concept description page. Firstly, we calculate the similarity between Chinese and Vietnamese news sentences, and filter the bilingual sentences accordingly. Then we use the filtered sentences as nodes and the similarity grade as the weight of the edge to construct an undirected graph model. Finally, combining the random walk algorithm, the weight of the node is calculated according to the weight of the edge, and sentences with highest weight can be extracted as the difference summary. The experiment results show that our proposed approach achieved the highest score of 0.1837 on the annotated test set, which outperforms the state-of-the-art summarization models.

Keywords

Acknowledgement

This work was supported by National key research and development plan project (No. 2018YFC0830105, 2018YFC0830100), National Nature Science Foundation (No. 61732005, 61672271, 61761026, 61662041, 61762056), High-tech Industry Development Project of Yunnan Province (No. 201606), and Natural Science Foundation of Yunnan Province (No. 2018FB104).

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