Abstract
The problem of text summarization has consistently been a significant and prominent challenge for a particular language. Each language’s unique characteristics will reflect that country’s identity, culture, and nuances. This paper introduces extractive text summarization models for Vietnamese documents. Our approach concentrates on discovering appreciative and plausible models by combining ML algorithms. Namely, we investigate three potential models, including a “G-global-hard-cluster” (with GloVe), “probability-cluster” (with LDA, Latent Dirichlet Allocation), and a “soft-specific” combination between SGD (Stochastic gradient descent) and kmeans. Moreover, we also provide experimental results to evaluate the quality of the summary and the consumption time. In particular, our approaches obtain the expected results with \(51.49\%\) ROUGE-1, \(17.99\%\) ROUGE-2, and \(29.25\%\) ROUGE-L. Finally, we discuss the promising results of the proposed models.
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Nguyen, TH., Ma, T., Do, TN. (2023). LAVETTES: Large-scAle-dataset Vietnamese ExTractive TExt Summarization Models. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_19
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