CALAMR: Component ALignment for Abstract Meaning Representation

Paul Landes, Barbara Di Eugenio


Abstract
We present Component ALignment for Abstract Meaning Representation (Calamr), a novel method for graph alignment that can support summarization and its evaluation. First, our method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners. Second, although numerous scoring methods have been proposed for abstract meaning representation (AMR) that evaluate semantic similarity, no AMR based summarization metrics exist despite years of work using AMR for this task. Calamr provides alignments on which new scores can be based. The contributions of this work include a) a novel approach to aligning AMR graphs, b) a new summarization based scoring methods for similarity of AMR subgraphs composed of one or more sentences, and c) the entire reusable source code to reproduce our results.
Anthology ID:
2024.lrec-main.236
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2622–2637
Language:
URL:
https://aclanthology.org/2024.lrec-main.236
DOI:
Bibkey:
Cite (ACL):
Paul Landes and Barbara Di Eugenio. 2024. CALAMR: Component ALignment for Abstract Meaning Representation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2622–2637, Torino, Italia. ELRA and ICCL.
Cite (Informal):
CALAMR: Component ALignment for Abstract Meaning Representation (Landes & Di Eugenio, LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.236.pdf