Summarizing sales calls is a routine task performed manually by salespeople.
We present a production system which combines generative models fine-tuned for
customer-agent setting, with a human-in-the-loop user experience for an
interactive summary curation process. We address challenging aspects of
dialogue summarization task in a real-world setting including long input
dialogues, content validation, lack of labeled data and quality evaluation. We
show how GPT-3 can be leveraged as an offline data labeler to handle training
data scarcity and accommodate privacy constraints in an industrial setting.
Experiments show significant improvements by our models in tackling the
summarization and content validation tasks on public datasets.

展示一种将 fine-tuned 的生成模型与人工的交互式摘要整理系统相结合的生产系统，以解决销售电话摘要的实际应用情景中存在的困难问题，包括长对话、内容验证、缺乏标记数据和质量评估，并说明了 GPT-3 可以用作离线数据标记器以解决数据贫乏和隐私限制等问题。在公共数据集上进行的实验表明，我们的模型在解决摘要和内容验证任务方面有了显著的改善。