Text Summarization With Graph Attention Networks
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,...
| Authors | Mohammadreza Ardestani, Yllias Chali |
|---|---|
| Primary category | cs.CL |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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