Pre-Training Clustering Models to Summarize Vietnamese Texts.
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| Title: | Pre-Training Clustering Models to Summarize Vietnamese Texts. |
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| Authors: | Nguyen, Ti-Hon, Do, Thanh-Nghi |
| Source: | Vietnam Journal of Computer Science (World Scientific); Feb2025, Vol. 12 Issue 1, p83-100, 18p |
| Subject Terms: | LANGUAGE models, AUTOMATIC summarization, TEXT summarization, DOCUMENT clustering, ELECTRONIC newspapers, K-means clustering, ALGORITHMS |
| Abstract: | Our investigation aims at pre-training clustering models to summarize Vietnamese texts. For this purpose, we create a large-scale dataset by collecting Vietnamese articles from newspaper websites and extracting the plain text to build the dataset, including 1,101,101 documents. We propose a new single-document extractive text summarization model based on clustering models. Our proposal clusters the documents with the hard clustering k-means algorithm and the soft clustering LDA (Latent Dirichlet Allocation) algorithm. Then, based on the pre-training clustering models, a summary model is used to select the salient sentence in the input text to construct the summary. The empirical results showed that our summary model achieved 51.22% ROUGE-1, 17.62% ROUGE-2 and 29.16% ROUGE-L on the testing set. Besides the traditional word representation such as BoW (Bag-of-Words), we also use the word meaning-based tools like FastText and BERT (Bidirectional Encoder Representations from Transformers) in our model. The additional benefit of our proposed extractive summary model is that the output summary is a long-text, readable document. Furthermore, the model's architecture is straightforward, easy to understand and runs on cost-efficient resources like arm CPU and GPU too. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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