title: BERT-VBD: Vietnamese Multi-Document Summarization Framework
summary: In tackling the challenge of Multi-Document Summarization (MDS), numerous
  methods have been proposed, spanning both extractive and abstractive
  summarization techniques. However, each approach has its own limitations,
  making it less effective to rely solely on either one. An emerging and
  promising strategy involves a synergistic fusion of extractive and abstractive
  summarization methods. Despite the plethora of studies in this domain, research
  on the combined methodology remains scarce, particularly in the context of
  Vietnamese language processing. This paper presents a novel Vietnamese MDS
  framework leveraging a two-component pipeline architecture that integrates
  extractive and abstractive techniques. The first component employs an
  extractive approach to identify key sentences within each document. This is
  achieved by a modification of the pre-trained BERT network, which derives
  semantically meaningful phrase embeddings using siamese and triplet network
  structures. The second component utilizes the VBD-LLaMA2-7B-50b model for
  abstractive summarization, ultimately generating the final summary document.
  Our proposed framework demonstrates a positive performance, attaining ROUGE-2
  scores of 39.6% on the VN-MDS dataset and outperforming the state-of-the-art
  baselines.
paper_authors._id: 66ec6d245a65f26be78a1e9a
paper_authors.name: Tuan-Cuong Vuong
paper_authors._id: 66ec6d245a65f26be78a1e9b
paper_authors.name: Trang Mai Xuan
paper_authors._id: 66ec6d245a65f26be78a1e9c
paper_authors.name: Thien Van Luong
paper_title: BERT-VBD: Vietnamese Multi-Document Summarization Framework
paper_summary: In tackling the challenge of Multi-Document Summarization (MDS), numerous
  methods have been proposed, spanning both extractive and abstractive
  summarization techniques. However, each approach has its own limitations,
  making it less effective to rely solely on either one. An emerging and
  promising strategy involves a synergistic fusion of extractive and abstractive
  summarization methods. Despite the plethora of studies in this domain, research
  on the combined methodology remains scarce, particularly in the context of
  Vietnamese language processing. This paper presents a novel Vietnamese MDS
  framework leveraging a two-component pipeline architecture that integrates
  extractive and abstractive techniques. The first component employs an
  extractive approach to identify key sentences within each document. This is
  achieved by a modification of the pre-trained BERT network, which derives
  semantically meaningful phrase embeddings using siamese and triplet network
  structures. The second component utilizes the VBD-LLaMA2-7B-50b model for
  abstractive summarization, ultimately generating the final summary document.
  Our proposed framework demonstrates a positive performance, attaining ROUGE-2
  scores of 39.6% on the VN-MDS dataset and outperforming the state-of-the-art
  baselines.
paper_ai_summary: A Vietnamese MDS framework combines extractive and abstractive methods using modified BERT and VBD-LLaMA2 models, achieving superior performance on the VN-MDS dataset.
paper_ai_keywords: BERT siamese network triplet network VBD-LLaMA2-7B-50b ROUGE-2