# Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs

arXiv: https://arxiv.org/abs/2104.08400 · HTML: https://mlsanity.com/paper/2104.08400/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [ReZero is All You Need: Fast Convergence at Large Depth](https://mlsanity.com/paper/2003.04887/) (2020)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2010.01672/) (2020)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [Multi-Fact Correction in Abstractive Text Summarization](https://mlsanity.com/paper/2010.02443/) (2020)
- [Structured Neural Summarization](https://mlsanity.com/paper/1811.01824/) (2018)
- [Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization](https://mlsanity.com/paper/1805.05271/) (2018)
- [Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts](https://mlsanity.com/paper/1809.05715/) (2018)
- [Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations](https://mlsanity.com/paper/1910.01335/) (2019)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [Incorporating Structured Commonsense Knowledge in Story Completion](https://mlsanity.com/paper/1811.00625/) (2018)
- [Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks](https://mlsanity.com/paper/2010.10044/) (2020)
- [From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information](https://mlsanity.com/paper/2005.04684/) (2020)
- [Using Summarization to Discover Argument Facets in Online Ideological Dialog](https://mlsanity.com/paper/1709.00662/) (2017)
- [Enhancing Factual Consistency of Abstractive Summarization](https://mlsanity.com/paper/2003.08612/) (2020)

## Built on by (most cited first, 30 of 36)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents](https://mlsanity.com/paper/2110.10150/) (2021)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning](https://mlsanity.com/paper/2112.08713/) (2021)
- [Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics](https://mlsanity.com/paper/2204.10216/) (2022)
- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [An Exploratory Study on Long Dialogue Summarization: What Works and What's Next](https://mlsanity.com/paper/2109.04609/) (2021)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [A Cheaper and Better Diffusion Language Model with Soft-Masked Noise](https://mlsanity.com/paper/2304.04746/) (2023)
- [PresentAgent: Multimodal Agent for Presentation Video Generation](https://mlsanity.com/paper/2507.04036/) (2025)
- [TODSum: Task-Oriented Dialogue Summarization with State Tracking](https://mlsanity.com/paper/2110.12680/) (2021)
- [Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization](https://mlsanity.com/paper/2305.16784/) (2023)
- [UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization](https://mlsanity.com/paper/2211.09783/) (2022)
- [Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization](https://mlsanity.com/paper/2204.04362/) (2022)
- [Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2209.00930/) (2022)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization](https://mlsanity.com/paper/2305.16548/) (2023)
- [EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics](https://mlsanity.com/paper/2408.08782/) (2024)
- [Human-in-the-loop Abstractive Dialogue Summarization](https://mlsanity.com/paper/2212.09750/) (2022)
- [Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework](https://mlsanity.com/paper/2306.05119/) (2023)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [Post-Training Dialogue Summarization using Pseudo-Paraphrasing](https://mlsanity.com/paper/2204.13498/) (2022)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System](https://mlsanity.com/paper/2312.16864/) (2023)
- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization](https://mlsanity.com/paper/2512.14687/) (2025)
- [He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues](https://mlsanity.com/paper/2210.15462/) (2022)

## Abstract (arXiv)

Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions. To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples ("who-doing-what") in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. Experiments show that our proposed models outperform state-of-the-art methods and generalize well in other domains in terms of both automatic evaluations and human judgments. We have publicly released our code at https://github.com/GT-SALT/Structure-Aware-BART.