%0 Conference Proceedings %T DocAMR: Multi-Sentence AMR Representation and Evaluation %A Naseem, Tahira %A Blodgett, Austin %A Kumaravel, Sadhana %A O’Gorman, Tim %A Lee, Young-Suk %A Flanigan, Jeffrey %A Astudillo, Ramón %A Florian, Radu %A Roukos, Salim %A Schneider, Nathan %Y Carpuat, Marine %Y de Marneffe, Marie-Catherine %Y Meza Ruiz, Ivan Vladimir %S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2022 %8 July %I Association for Computational Linguistics %C Seattle, United States %F naseem-etal-2022-docamr %X Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research. %R 10.18653/v1/2022.naacl-main.256 %U https://aclanthology.org/2022.naacl-main.256 %U https://doi.org/10.18653/v1/2022.naacl-main.256 %P 3496-3505