Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
<|reference_start|>Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection: 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}.<|reference_end|>
@article{he2024semi-supervised,
    title={Semi-Supervised Dialogue Abstractive Summarization via High-Quality
  Pseudolabel Selection},
    author={Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, Saab
  Mansour},
    journal={arXiv preprint arXiv:2403.04073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04073},
    primaryClass={cs.CL cs.AI}
}