Text Summarization With Graph Attention Networks

• URL: http://arxiv.org/abs/2604.03583v1
• Date: Sat, 04 Apr 2026 04:17:29 GMT
• Title: Text Summarization With Graph Attention Networks
• Abstract summary: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models.<n>We used a simple Multi-layer Perceptron architecture, which improved the results in our proposed model on our primary dataset, CNN/DM.
• Score: 3.9103337761169943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models. Specifically, we experimented with a Graph Attention Network architecture to incorporate graph information. However, this architecture did not enhance the performance. Subsequently, we used a simple Multi-layer Perceptron architecture, which improved the results in our proposed model on our primary dataset, CNN/DM. Additionally, we annotated XSum dataset with RST graph information, establishing a benchmark for future graph-based summarization models. This secondary dataset posed multiple challenges, revealing both the merits and limitations of our models.

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