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

Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, Saab Mansour

Abstract
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 https://github.com/amazon-science/summarization-sicf-score.
Anthology ID:
2024.naacl-long.333
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5976–5996
Language:
URL:
https://aclanthology.org/2024.naacl-long.333
DOI:
10.18653/v1/2024.naacl-long.333
Bibkey:
Cite (ACL):
Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, and Saab Mansour. 2024. Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5976–5996, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection (He et al., NAACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.333.pdf
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