# Abstract Meaning Representation for Multi-Document Summarization

arXiv: https://arxiv.org/abs/1806.05655 · HTML: https://mlsanity.com/paper/1806.05655/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 30 of 35)

- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Structural Adapters in Pretrained Language Models for AMR-to-text Generation](https://mlsanity.com/paper/2103.09120/) (2021)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations](https://mlsanity.com/paper/2203.09711/) (2022)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://mlsanity.com/paper/2104.05022/) (2021)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering](https://mlsanity.com/paper/2105.11776/) (2021)
- [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021)
- [Exploiting Reasoning Chains for Multi-hop Science Question Answering](https://mlsanity.com/paper/2109.02905/) (2021)
- [QASem Parsing: Text-to-text Modeling of QA-based Semantics](https://mlsanity.com/paper/2205.11413/) (2022)
- [Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering](https://mlsanity.com/paper/2206.08486/) (2022)
- [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://mlsanity.com/paper/2210.12654/) (2022)
- [Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization](https://mlsanity.com/paper/2106.01317/) (2021)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [Better Smatch = Better Parser? AMR evaluation is not so simple anymore](https://mlsanity.com/paper/2210.06461/) (2022)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [Incorporating Graph Information in Transformer-based AMR Parsing](https://mlsanity.com/paper/2306.13467/) (2023)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://mlsanity.com/paper/2212.01667/) (2022)
- [An analysis of document graph construction methods for AMR summarization](https://mlsanity.com/paper/2111.13993/) (2021)
- [ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs](https://mlsanity.com/paper/2204.08875/) (2022)
- [Visual Semantic Parsing: From Images to Abstract Meaning Representation](https://mlsanity.com/paper/2210.14862/) (2022)
- [AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://mlsanity.com/paper/2306.10786/) (2023)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04814/) (2021)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [Cross-lingual AMR Aligner: Paying Attention to Cross-Attention](https://mlsanity.com/paper/2206.07587/) (2022)

## Abstract (arXiv)

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.