# Text Summarization using Abstract Meaning Representation

arXiv: https://arxiv.org/abs/1706.01678 · HTML: https://mlsanity.com/paper/1706.01678/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

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

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- [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)
- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [Better Smatch = Better Parser? AMR evaluation is not so simple anymore](https://mlsanity.com/paper/2210.06461/) (2022)
- [AMREx: AMR for Explainable Fact Verification](https://mlsanity.com/paper/2411.01343/) (2024)
- [Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing](https://mlsanity.com/paper/2110.00621/) (2021)
- [Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings](https://mlsanity.com/paper/2206.06229/) (2022)
- [Evaluation of Finetuned LLMs in AMR Parsing](https://mlsanity.com/paper/2508.05028/) (2025)
- [Approaching the Source of Symbol Grounding with Confluent Reductions of Abstract Meaning Representation Directed Graphs](https://mlsanity.com/paper/2508.11068/) (2025)
- [Gyan: An Explainable Neuro-Symbolic Language Model](https://mlsanity.com/paper/2605.04759/) (2026)
- [Assessing Quality of Experience in Natural Language Generation of German Text](https://mlsanity.com/paper/2608.18888/) (2026)

## Abstract (arXiv)

With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it extracts a summary graph and finally, generate summary sentences from this summary graph. Our proposed method achieves state-of-the-art results compared to the other text summarization routines based on AMR. We also point out some significant problems in the existing evaluation methods, which make them unsuitable for evaluating summary quality.