Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task

TL;DR

This paper presents a graph-based extractive multi-document summarization method for Vietnamese, transforming the problem into subgraph selection to better capture sentence relationships, achieving top results in VLSP 2022.

Contribution

It introduces a novel subgraph selection approach for multi-document summarization that leverages graph structures to improve summary quality.

Findings

Achieved top 10 ranking on ROUGH-2 F1 measure in VLSP 2022.

Demonstrated effectiveness of graph-based subgraph selection in Vietnamese MDS.

Outperformed several baseline methods in the shared task.

Abstract

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.