TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
This work addresses the need for efficient summarization in customer support to aid agents, but it is incremental as it builds on existing summarization techniques with a new dataset.
The authors tackled the problem of automating customer service dialog summarization by introducing TWEETSUMM, a dataset of nearly 6500 human-annotated summaries based on real-world dialogs, and they proposed a new unsupervised extractive method for this task.
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.