TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

TL;DR

This paper introduces TWEETSUMM, a large-scale dataset of customer service chat summaries, and proposes a new unsupervised extractive summarization method tailored for dialogs, advancing automated customer support summarization.

Contribution

The paper provides the first large, high-quality customer care dialog dataset with annotated summaries and introduces a novel unsupervised extractive summarization approach for dialogs.

Findings

The dataset contains nearly 6500 annotated summaries.

The proposed method outperforms existing baselines on dialog summarization.

The dataset enables training and evaluation of automated summarization systems.

Abstract

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.