Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task
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Abstract (arXiv)
Document summarization is a task to generate afluent, condensed summary for a document, andkeep important information. A cluster of documents serves as the input for multi-document summarizing (MDS), while the cluster summary serves as the output. In this paper, we focus on transforming the extractive MDS problem into subgraph selection. Approaching the problem in the form of graphs helps to capture simultaneously the relationship between sentences in the same document and between sentences in the same cluster based on exploiting the overall graph structure and selected subgraphs. Experiments have been implemented on the Vietnamese dataset published in VLSP Evaluation Campaign 2022. This model currently results in the top 10 participating teams reported on the ROUGH-2 F1 measure on the public test set.
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References · 7
- 2017Attention Is All You Needcited by 60,361
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 246
- 2019Hierarchical Transformers for Multi-Document Summarizationcited by 132
- 2020Leveraging Graph to Improve Abstractive Multi-Document Summarizationcited by 67
- 2016Improving Multi-Document Summarization via Text Classificationcited by 14
- 2021SgSum: Transforming Multi-document Summarization into Sub-graph Selectioncited by 8
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