# A Survey on Dialogue Summarization: Recent Advances and New Frontiers

arXiv: https://arxiv.org/abs/2107.03175 · HTML: https://mlsanity.com/paper/2107.03175/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 148 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 30 of 57)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (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)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [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 Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021)
- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)
- [SummScreen: A Dataset for Abstractive Screenplay Summarization](https://mlsanity.com/paper/2104.07091/) (2021)
- [The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey](https://mlsanity.com/paper/2104.14839/) (2021)
- [GO FIGURE: A Meta Evaluation of Factuality in Summarization](https://mlsanity.com/paper/2010.12834/) (2020)
- [Planning with Learned Entity Prompts for Abstractive Summarization](https://mlsanity.com/paper/2104.07606/) (2021)
- [Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2010.01672/) (2020)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://mlsanity.com/paper/1906.03497/) (2019)
- [Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://mlsanity.com/paper/1807.02305/) (2018)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)

## Built on by (most cited first, 30 of 51)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning](https://mlsanity.com/paper/2112.08713/) (2021)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [DialogBench: Evaluating LLMs as Human-like Dialogue Systems](https://mlsanity.com/paper/2311.01677/) (2023)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [Towards LifeSpan Cognitive Systems](https://mlsanity.com/paper/2409.13265/) (2024)
- [Analyzing and Evaluating Faithfulness in Dialogue Summarization](https://mlsanity.com/paper/2210.11777/) (2022)
- [PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records](https://mlsanity.com/paper/2307.02006/) (2023)
- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [What's under the hood: Investigating Automatic Metrics on Meeting Summarization](https://mlsanity.com/paper/2404.11124/) (2024)
- [The Cross-lingual Conversation Summarization Challenge](https://mlsanity.com/paper/2205.00379/) (2022)
- [Human-in-the-loop Abstractive Dialogue Summarization](https://mlsanity.com/paper/2212.09750/) (2022)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)
- [Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization](https://mlsanity.com/paper/2310.10285/) (2023)
- [A Focused Study on Sequence Length for Dialogue Summarization](https://mlsanity.com/paper/2209.11910/) (2022)
- [DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization](https://mlsanity.com/paper/2212.10018/) (2022)
- [Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization](https://mlsanity.com/paper/2308.03275/) (2023)
- [Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection](https://mlsanity.com/paper/2403.04073/) (2024)
- [Post-Training Dialogue Summarization using Pseudo-Paraphrasing](https://mlsanity.com/paper/2204.13498/) (2022)
- [Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues](https://mlsanity.com/paper/2302.05895/) (2023)
- [Dialogue Inspectional Summarization with Factual Inconsistency Awareness](https://mlsanity.com/paper/2111.03284/) (2021)
- [Effectiveness of French Language Models on Abstractive Dialogue Summarization Task](https://mlsanity.com/paper/2207.08305/) (2022)
- [Leveraging Non-dialogue Summaries for Dialogue Summarization](https://mlsanity.com/paper/2210.09474/) (2022)
- [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)

## Abstract (arXiv)

Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize the current works according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue. Additionally, we provide an overview of publicly available research datasets as well as organize two leaderboards under unified metrics. Furthermore, we discuss some future directions, including faithfulness, multi-modal, multi-domain and multi-lingual dialogue summarization, and give our thoughts respectively. We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.