# Human-in-the-loop Abstractive Dialogue Summarization

arXiv: https://arxiv.org/abs/2212.09750 · HTML: https://mlsanity.com/paper/2212.09750/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog](https://mlsanity.com/paper/1907.00456/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://mlsanity.com/paper/1901.05415/) (2019)
- [Better Rewards Yield Better Summaries: Learning to Summarise Without References](https://mlsanity.com/paper/1909.01214/) (2019)
- [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)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [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)
- [Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://mlsanity.com/paper/2104.08400/) (2021)
- [Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators](https://mlsanity.com/paper/1904.13015/) (2019)
- [Coreference-Aware Dialogue Summarization](https://mlsanity.com/paper/2106.08556/) (2021)
- [Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models](https://mlsanity.com/paper/2002.05058/) (2020)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks](https://mlsanity.com/paper/2010.10044/) (2020)
- [Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation](https://mlsanity.com/paper/1907.12894/) (2019)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring](https://mlsanity.com/paper/1903.03530/) (2019)
- [RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation](https://mlsanity.com/paper/2106.04080/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback](https://mlsanity.com/paper/2305.08982/) (2023)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection](https://mlsanity.com/paper/2403.04073/) (2024)
- [How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond](https://mlsanity.com/paper/2501.05714/) (2025)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization](https://mlsanity.com/paper/2407.14000/) (2024)
- [Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts](https://mlsanity.com/paper/2412.19966/) (2024)

## Abstract (arXiv)

Abstractive dialogue summarization has received increasing attention recently. Despite the fact that most of the current dialogue summarization systems are trained to maximize the likelihood of human-written summaries and have achieved significant results, there is still a huge gap in generating high-quality summaries as determined by humans, such as coherence and faithfulness, partly due to the misalignment in maximizing a single human-written summary. To this end, we propose to incorporate different levels of human feedback into the training process. This will enable us to guide the models to capture the behaviors humans care about for summaries. Specifically, we ask humans to highlight the salient information to be included in summaries to provide the local feedback , and to make overall comparisons among summaries in terms of coherence, accuracy, coverage, concise and overall quality, as the global feedback. We then combine both local and global feedback to fine-tune the dialog summarization policy with Reinforcement Learning. Experiments conducted on multiple datasets demonstrate the effectiveness and generalization of our methods over the state-of-the-art supervised baselines, especially in terms of human judgments.