# Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

arXiv: https://arxiv.org/abs/2012.03502 · HTML: https://mlsanity.com/paper/2012.03502/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 28 of 28)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [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)
- [Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization](https://mlsanity.com/paper/2105.12544/) (2021)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2402.15080/) (2024)
- [Medical Dialogue Response Generation with Pivotal Information Recalling](https://mlsanity.com/paper/2206.08611/) (2022)
- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics](https://mlsanity.com/paper/2408.08782/) (2024)
- [Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks](https://mlsanity.com/paper/2201.12538/) (2022)
- [Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization](https://mlsanity.com/paper/2308.03275/) (2023)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Meeting Summarization: A Survey of the State of the Art](https://mlsanity.com/paper/2212.08206/) (2022)
- [Leveraging Discourse Structure for Extractive Meeting Summarization](https://mlsanity.com/paper/2405.11055/) (2024)
- [Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue](https://mlsanity.com/paper/2405.19799/) (2024)
- [Building Knowledge-Grounded Dialogue Systems with Graph-Based Semantic Modeling](https://mlsanity.com/paper/2204.12681/) (2022)
- [GOAL: Towards Benchmarking Few-Shot Sports Game Summarization](https://mlsanity.com/paper/2207.08635/) (2022)
- [The Role of Summarization in Generative Agents: A Preliminary Perspective](https://mlsanity.com/paper/2305.01253/) (2023)
- [Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification](https://mlsanity.com/paper/2311.04292/) (2023)
- [Automated Speaking Assessment of Conversation Tests with Novel Graph-based Modeling on Spoken Response Coherence](https://mlsanity.com/paper/2409.07064/) (2024)
- [From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation](https://mlsanity.com/paper/2506.11557/) (2025)
- [Meeting Summarization with Pre-training and Clustering Methods](https://mlsanity.com/paper/2111.08210/) (2021)
- [STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension](https://mlsanity.com/paper/2212.12652/) (2022)
- [Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts](https://mlsanity.com/paper/2305.07988/) (2023)
- [Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization](https://mlsanity.com/paper/2604.17188/) (2026)
- [DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset](https://mlsanity.com/paper/2606.00012/) (2026)
- [Structure-Preserving Document-Level Data Augmentation via Discourse Parsing](https://mlsanity.com/paper/2610.19497/) (2026)

## Abstract (arXiv)

Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance. Besides, the limited labeled data further hinders the ability of data-hungry neural models. In this paper, we try to mitigate the above challenges by introducing dialogue-discourse relations. First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations. The core module is a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner. Moreover, we devise a Dialogue Discourse-Aware Data Augmentation (DDADA) strategy to construct a pseudo-summarization corpus from existing input meetings, which is 20 times larger than the original dataset and can be used to pretrain DDAMS. Experimental results on AMI and ICSI meeting datasets show that our full system can achieve SOTA performance. Our codes will be available at: https://github.com/xcfcode/DDAMS.