Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task
This work addresses multi-document summarization for Vietnamese, an incremental improvement using graph-based methods on a specific dataset.
The paper tackled extractive multi-document summarization for Vietnamese by transforming it into a subgraph selection problem, achieving a top-10 ranking among participating teams on the ROUGH-2 F1 measure in the VLSP 2022 shared task.
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 $F\_1$ measure on the public test set.