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

Understand

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).