{"title":"CALAMR: Component ALignment for Abstract Meaning Representation.","authors":"Paul Landes, Barbara Di Eugenio","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":91924,"journal":{"name":"LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation","volume":"2024 LREC-COLING","pages":"2622-2637"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11627045/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.
摘要提出了一种新的图形对齐方法——面向抽象意义表示的组件对齐（Component ALignment for Abstract Meaning Representation, Calamr）。首先，我们的方法生成图表，通过它们的对齐来解释总结的内容，这可以用于训练基于图表的摘要学习器。其次，尽管已经为评估语义相似性的抽象意义表示（AMR）提出了许多评分方法，但尽管使用AMR进行了多年的工作，但还没有基于AMR的摘要度量。Calamr提供了新的分数可以基于的排列。这项工作的贡献包括a)一种新的对齐AMR图的方法，b)一种新的基于摘要的AMR子图相似性评分方法，以及c)完整的可重用源代码来重现我们的结果。
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