TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
- URL: http://arxiv.org/abs/2111.11894v1
- Date: Tue, 23 Nov 2021 14:13:51 GMT
- Title: TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
- Authors: Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra
Joshi, David Konopnicki and Ranit Aharonov
- Abstract summary: 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.
- Score: 13.661851509322455
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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