# Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection

arXiv: https://arxiv.org/abs/2403.04073 · HTML: https://mlsanity.com/paper/2403.04073/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 48 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 30 of 34)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (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)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://mlsanity.com/paper/2210.07197/) (2022)
- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [Revisiting Self-Training for Neural Sequence Generation](https://mlsanity.com/paper/1909.13788/) (2019)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://mlsanity.com/paper/2106.09449/) (2021)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [Better Zero-Shot Reasoning with Self-Adaptive Prompting](https://mlsanity.com/paper/2305.14106/) (2023)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [Controllable Neural Dialogue Summarization with Personal Named Entity Planning](https://mlsanity.com/paper/2109.13070/) (2021)
- [Noisy Self-Knowledge Distillation for Text Summarization](https://mlsanity.com/paper/2009.07032/) (2020)
- [A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?](https://mlsanity.com/paper/2012.03061/) (2020)
- [Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection](https://mlsanity.com/paper/2402.11406/) (2024)
- [Mitigating Uncertainty in Document Classification](https://mlsanity.com/paper/1907.07590/) (2019)
- [TODSum: Task-Oriented Dialogue Summarization with State Tracking](https://mlsanity.com/paper/2110.12680/) (2021)
- [Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2108.02859/) (2021)
- [SWING: Balancing Coverage and Faithfulness for Dialogue Summarization](https://mlsanity.com/paper/2301.10483/) (2023)
- [Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission](https://mlsanity.com/paper/2311.08726/) (2023)
- [Human-in-the-loop Abstractive Dialogue Summarization](https://mlsanity.com/paper/2212.09750/) (2022)

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

- [Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization](https://mlsanity.com/paper/2502.17328/) (2025)
- [Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?](https://mlsanity.com/paper/2406.17274/) (2024)
- [Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization](https://mlsanity.com/paper/2512.14687/) (2025)
- [CERET: Cost-Effective Extrinsic Refinement for Text Generation](https://mlsanity.com/paper/2406.05588/) (2024)
- [Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence](https://mlsanity.com/paper/2412.00883/) (2024)

## Abstract (arXiv)

Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models. While addressing label noise, previous works on semi-supervised learning primarily focus on natural language understanding tasks, assuming each sample has a unique label. However, these methods are not directly applicable to SSDS, as it is a generative task, and each dialogue can be summarized in different ways. In this work, we propose a novel scoring approach, SiCF, which encapsulates three primary dimensions of summarization model quality: Semantic invariance (indicative of model confidence), Coverage (factual recall), and Faithfulness (factual precision). Using the SiCF score, we select unlabeled dialogues with high-quality generated summaries to train summarization models. Comprehensive experiments on three public datasets demonstrate the effectiveness of SiCF scores in uncertainty estimation and semi-supervised learning for dialogue summarization tasks. Our code is available at urlhttps://github.com/amazon-science/summarization-sicf-score.