Text Summarization With Graph Attention Networks
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.
Cite
@article{arxiv.2604.03583,
title = {Text Summarization With Graph Attention Networks},
author = {Mohammadreza Ardestani and Yllias Chali},
journal= {arXiv preprint arXiv:2604.03583},
year = {2026}
}
Comments
Published in Proceedings of the 4th NeurIPS Efficient Natural Language and Speech Processing Workshop (ENLSP-IV), Vancouver, Canada, 2024. 14 pages, 8 figures