# DocAMR: Multi-Sentence AMR Representation and Evaluation

arXiv: https://arxiv.org/abs/2112.08513 · HTML: https://mlsanity.com/paper/2112.08513/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Anaphora and Coreference Resolution: A Review](https://mlsanity.com/paper/1805.11824/) (2018)
- [Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing](https://mlsanity.com/paper/2110.15534/) (2021)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [Compositional Semantic Parsing Across Graphbanks](https://mlsanity.com/paper/1906.11746/) (2019)
- [An analysis of document graph construction methods for AMR summarization](https://mlsanity.com/paper/2111.13993/) (2021)

## Built on by (most cited first, 12 of 12)

- [Don't Forget to Connect! Improving RAG with Graph-based Reranking](https://mlsanity.com/paper/2405.18414/) (2024)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Visual Semantic Parsing: From Images to Abstract Meaning Representation](https://mlsanity.com/paper/2210.14862/) (2022)
- [DART: An AIGT Detector using AMR of Rephrased Text](https://mlsanity.com/paper/2412.11517/) (2024)
- [NECE: Narrative Event Chain Extraction Toolkit](https://mlsanity.com/paper/2208.08063/) (2022)
- [Exploiting Abstract Meaning Representation for Open-Domain Question Answering](https://mlsanity.com/paper/2305.17050/) (2023)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity](https://mlsanity.com/paper/2404.02126/) (2024)
- [Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing](https://mlsanity.com/paper/2305.17273/) (2023)
- [FLAG: Financial Long Document Classification via AMR-based GNN](https://mlsanity.com/paper/2410.02024/) (2024)
- [Abstract Meaning Representation for Hospital Discharge Summarization](https://mlsanity.com/paper/2506.14101/) (2025)
- [Evaluation of Finetuned LLMs in AMR Parsing](https://mlsanity.com/paper/2508.05028/) (2025)

## Abstract (arXiv)

Despite extensive research on parsing of English sentences into Abstraction 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.