Papers by Margarita Bugueño
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations (2026.eacl-long)
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Yindong Wang, Martin Preiß, Margarita Bugueño, Jan Vincent Hoffbauer, Abdullatif Ghajar, Tolga Buz, Gerard de Melo
| Challenge: | Evaluating 9 state-of-the-art LLMs reveals two critical limitations: 61% of incorrect span predictions are semantically unrelated to actual errors. |
| Approach: | They propose a benchmark of 1,001 expert-annotated question-answer pairs with span-level error annotations derived from Reddit's r/AskScience. |
| Outcome: | Evaluating 9 state-of-the-art LLMs, we find that comparative judgment is paradoxically harder than independent detection when comparing answers side-by-side. |
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization (2025.naacl-short)
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| Challenge: | Graph-based methods for extracting documents have been popular, but they often require external tools or additional machine learning models to define graph components. |
| Approach: | They propose a heterogeneous graph construction for extractive summarization that defines two levels of information and four types of edges without any need for auxiliary learning models. |
| Outcome: | The proposed graph construction outperforms previous graph-based models on two datasets and is available on GitHub. |