Semi-Supervised Dialogue Abstractive Summarization via High-Quality
  Pseudolabel Selection
Jianfeng He Hang Su Jason Cai Igor Shalyminov Hwanjun Song Saab Mansour
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 \url{https://github.com/amazon-science/summarization-sicf-score}.