论文标题
抽象性会议摘要：调查
Abstractive Meeting Summarization: A Survey
论文作者
论文摘要
可以可靠地识别并总结对话中最重要的一点的系统在从商务会议到医疗咨询再到客户服务电话的各种现实环境中都很有价值。深度学习的最新进展，尤其是编码器架构的发明，具有显着改善的语言生成系统，为改进的抽象性摘要形式打开了大门，这是一种特别适合多方对话的摘要形式。在本文中，我们概述了抽象性会议摘要的任务以及用于解决问题的数据集，模型和评估指标所面临的挑战。
A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization, a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models and evaluation metrics that have been used to tackle the problems.
