Factual inconsistencies in generated summaries severely limit the practical
applications of abstractive dialogue summarization. Although significant
progress has been achieved by using pre-trained models, substantial amounts of
hallucinated content are found during the human evaluation. Pre-trained models
are most commonly fine-tuned with cross-entropy loss for text summarization,
which may not be an optimal strategy. In this work, we provide a typology of
factual errors with annotation data to highlight the types of errors and move
away from a binary understanding of factuality. We further 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. Specifically, we utilize hard
negative samples with errors to reduce the generation of factual inconsistency.
In order to capture the key information between speakers, we also design a
dialogue-specific loss. Using human evaluation and automatic faithfulness
metrics, we show that our model significantly reduces all kinds of factual
errors on the dialogue summarization, SAMSum corpus. Moreover, our model could
be generalized to the meeting summarization, AMI corpus, and it produces
significantly higher scores than most of the baselines on both datasets
regarding word-overlap metrics.

通过 ConFiT 训练方法及一系列的模块化目标来改善抽象型对话摘要的事实准确性，最终生成的摘要数据质量得到了显著提高。