Dialog summarization has become increasingly important in managing and comprehending large-scale conversations across various domains. This task presents unique challenges in capturing the key points, context, and nuances of multi-turn long conversations for summarization. It is worth noting that the summarization techniques may vary based on specific requirements such as in a shopping-chatbot scenario, the dialog summary helps to learn user preferences, whereas in the case of a customer call center, the summary may involve the problem attributes that a user specified, and the final resolution provided. This work emphasizes the significance of creating coherent and contextually rich summaries for effective communication in various applications. We explore current state-of-the-art approaches for long dialog summarization in different domains and benchmark metrics based evaluations show that one single model does not perform well across various areas for distinct summarization tasks.

翻译：对话摘要在管理和理解跨领域大规模对话中日益重要。该任务在捕捉多轮长对话的关键点、语境和细微差别以进行摘要方面面临独特挑战。值得注意的是，摘要技术可能因具体需求而异，例如在购物聊天机器人场景中，对话摘要有助于学习用户偏好；而在客户呼叫中心场景中，摘要可能涉及用户指定的问题属性及提供的最终解决方案。本研究强调了为各类应用中的有效沟通生成连贯且语境丰富的摘要的重要性。我们探讨了不同领域内用于长对话摘要的当前最先进方法，基于基准指标的评估表明，单一模型在针对不同摘要任务的跨领域场景中表现不佳。