Text Summarization on Large-scale Vietnamese Datasets

This investigation is aimed at automatic text summarization on large-scale Vietnamese datasets.Vietnamese articles were collected from newspaper websites and plain text was extracted to build the dataset, that included 1,101,101 documents.Next, a new single-document extractive text summarization model was proposed to evaluate this dataset.In this summary model, the kmeans algorithm is used to cluster the sentences of the input document using different text representations, such as BoW (bag-ofwords), TF-IDF (term frequency -inverse document frequency), Word2Vec (Word-to-vector), Glove, and FastText.The summary algorithm then uses the trained k-means model to rank the candidate sentences and create a summary with the highestranked sentences.The empirical results of the F1-score achieved 51.91% ROUGE-1, 18.77% ROUGE-2 and 29.72% ROUGE-L, compared to 52.33% ROUGE-1, 16.17% ROUGE-2, and 33.09% ROUGE-L performed using a competitive abstractive model.The advantage of the proposed model is that it can perform well with O(n,k,p) = O(n (k + 2/p) ) + O(nlog 2 n) + O(np) + O(nk 2 ) + O(k) time complexity.