# Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

arXiv: https://arxiv.org/abs/2009.06851 · HTML: https://mlsanity.com/paper/2009.06851/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [An End-to-End Dialogue Summarization System for Sales Calls](https://mlsanity.com/paper/2204.12951/) (2022)
- [Scientific Paper Extractive Summarization Enhanced by Citation Graphs](https://mlsanity.com/paper/2212.04214/) (2022)
- [Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality](https://mlsanity.com/paper/2301.12376/) (2023)
- [LLM aided semi-supervision for Extractive Dialog Summarization](https://mlsanity.com/paper/2311.11462/) (2023)
- [ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation](https://mlsanity.com/paper/2205.05433/) (2022)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion](https://mlsanity.com/paper/2205.13108/) (2022)
- [User Review Writing via Interview with Dialogue Systems](https://mlsanity.com/paper/2603.07070/) (2026)
- [Topic-Guided Self-Introduction Generation for Social Media Users](https://mlsanity.com/paper/2305.15138/) (2023)
- [SumRec: A Framework for Recommendation using Open-Domain Dialogue](https://mlsanity.com/paper/2402.04523/) (2024)

## Abstract (arXiv)

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation.