LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization
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Abstract (arXiv)
Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents. This paper proposes a method for multi-document summarization based on cluster similarity. In the extractive method we use hybrid model based on a modified version of the PageRank algorithm and a text correlation considerations mechanism. After generating summaries by selecting the most important sentences from each cluster, we apply BARTpho and ViT5 to construct the abstractive models. Both extractive and abstractive approaches were considered in this study. The proposed method achieves competitive results in VLSP 2022 competition.
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References · 10
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2020PhoBERT: Pre-trained language models for Vietnamesecited by 148
- 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
- 2019A Deep Look into Neural Ranking Models for Information Retrievalcited by 79
- 2017Graph-based Neural Multi-Document Summarizationcited by 54
- 2018VnCoreNLP: A Vietnamese Natural Language Processing Toolkitcited by 51
- 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37
- 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 33
- 2022Transformer-based Approaches for Legal Text Processingcited by 3
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