# Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization

arXiv: https://arxiv.org/abs/2108.01268 · HTML: https://mlsanity.com/paper/2108.01268/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2010.01672/) (2020)
- [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)
- [Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks](https://mlsanity.com/paper/2010.10044/) (2020)
- [Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling](https://mlsanity.com/paper/1906.06893/) (2019)
- [Abstractive Dialog Summarization with Semantic Scaffolds](https://mlsanity.com/paper/1910.00825/) (2019)
- [Dialog System Technology Challenge 7](https://mlsanity.com/paper/1901.03461/) (2019)
- [Dial2Desc: End-to-end Dialogue Description Generation](https://mlsanity.com/paper/1811.00185/) (2018)

## Built on by (most cited first, 5 of 5)

- [CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning](https://mlsanity.com/paper/2112.08713/) (2021)
- [SWING: Balancing Coverage and Faithfulness for Dialogue Summarization](https://mlsanity.com/paper/2301.10483/) (2023)
- [Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization](https://mlsanity.com/paper/2305.01951/) (2023)
- [Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection](https://mlsanity.com/paper/2403.04073/) (2024)
- [Language Model Pre-training on True Negatives](https://mlsanity.com/paper/2212.00460/) (2022)

## Abstract (arXiv)

Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization with two novel modules, namely, the supporting utterance flow modeling module and the fact regularization module. The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training, which helps to improve the factual correctness of the generated summary in inference time. Furthermore, we also introduce a new benchmark dataset for dialogue summarization. Extensive experiments on both existing and newly-introduced datasets demonstrate the effectiveness of our model.