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CALAMR: Component ALignment for Abstract Meaning Representation
Paul Landes1, Barbara Di Eugenio1
1Department of Computer Science, University of Illinois Chicago.
LREC ... International Conference on Language Resources & Evaluation : [Proceedings]. International Conference on Language Resources & Evaluation
|December 10, 2024
Summary
We introduce Component ALignment for Abstract Meaning Representation (Calamr), a new graph alignment method. Calamr aids in training summarization models and developing novel evaluation metrics for Abstract Meaning Representation graphs.
Area of Science:
- Natural Language Processing
- Graph Theory
- Computational Linguistics
Background:
- Abstract Meaning Representation (AMR) is used to represent sentence meaning.
- Evaluating semantic similarity in AMR is challenging.
- Existing AMR research lacks summarization-specific evaluation metrics.
Purpose of the Study:
- Introduce Component ALignment for Abstract Meaning Representation (Calamr), a novel graph alignment method.
- Enable training of graph-based summarization learners using AMR alignments.
- Develop new scoring methods for AMR-based summarization evaluation.
Main Methods:
- Developed a novel approach for aligning AMR graphs.
- Generated aligned AMR graphs to explain summarization.
- Created new scoring methods based on AMR subgraph similarity.
Main Results:
- Calamr produces explanatory graphs through alignments.
- The method supports training of graph-based summarization models.
- New summarization-based scoring methods for AMR similarity were developed.
Conclusions:
- Calamr offers a new method for AMR graph alignment.
- The approach facilitates the creation of AMR-based summarization metrics.
- Reusable source code is provided to ensure reproducibility.
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