6 citations · 6 across the 5 of their papers we have counts for
5 papers
From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
Ruangrin Ldallitsakool, Margarita Bugueño, Gerard de Melo
Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global docume…
ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations
Yindong Wang, Martin Preiß, Margarita Bugueño +4
The mechanisms underlying scientific confabulation in Large Language Models (LLMs) remain poorly understood. We introduce ReFACT (Reddit False And Correct Texts), a benchmark of 1,…
Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches
Margarita Bugueño, Gerard de Melo
In document classification, graph-based models effectively capture document structure, overcoming sequence length limitations and enhancing contextual understanding. However, most…
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization
Margarita Bugueño, Hazem Abou Hamdan, Gerard de Melo
Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these…
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?
Margarita Bugueño, Gerard de Melo
Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains…