We propose a training strategy that improves the factual consistency and overall quality of summaries via a novel contrastive fine-tuning, called ConFiT. Based on our linguistically-informed typology of errors, we design different modular objectives that each target a specific type.
@article{tang2021confit,
title={CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning},
author={Tang, Xiangru and Nair, Arjun and Wang, Borui and Wang, Bingyao and Desai, Jai and Wade, Aaron and Li, Haoran and Celikyilmaz, Asli and Mehdad, Yashar and Radev, Dragomir},
journal={arXiv preprint arXiv:2112.08713},
year={2021}
}