# Toward Abstractive Summarization Using Semantic Representations

arXiv: https://arxiv.org/abs/1805.10399 · HTML: https://mlsanity.com/paper/1805.10399/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 30 of 43)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Tractable Control for Autoregressive Language Generation](https://mlsanity.com/paper/2304.07438/) (2023)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [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)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset](https://mlsanity.com/paper/2107.09556/) (2021)
- [Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering](https://mlsanity.com/paper/2105.11776/) (2021)
- [Automatic Text Summarization Methods: A Comprehensive Review](https://mlsanity.com/paper/2204.01849/) (2022)
- [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021)
- [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://mlsanity.com/paper/2112.08513/) (2021)
- [QASem Parsing: Text-to-text Modeling of QA-based Semantics](https://mlsanity.com/paper/2205.11413/) (2022)
- [Analyzing the Role of Semantic Representations in the Era of Large Language Models](https://mlsanity.com/paper/2405.01502/) (2024)
- [Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs](https://mlsanity.com/paper/2206.07296/) (2022)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Diffusion-based Human Motion Style Transfer with Semantic Guidance](https://mlsanity.com/paper/2405.06646/) (2024)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [Abstractive Summarization of Large Document Collections Using GPT](https://mlsanity.com/paper/2310.05690/) (2023)
- [An analysis of document graph construction methods for AMR summarization](https://mlsanity.com/paper/2111.13993/) (2021)
- [Visual Semantic Parsing: From Images to Abstract Meaning Representation](https://mlsanity.com/paper/2210.14862/) (2022)
- [Dialogue Inspectional Summarization with Factual Inconsistency Awareness](https://mlsanity.com/paper/2111.03284/) (2021)
- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing](https://mlsanity.com/paper/2410.03357/) (2024)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework](https://mlsanity.com/paper/2509.24821/) (2025)
- [Concept than Document: Context Compression via AMR-based Conceptual Entropy](https://mlsanity.com/paper/2511.18832/) (2025)
- [Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04814/) (2021)

## Abstract (arXiv)

We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization