TWEETSUMM - A Dialog Summarization Dataset for Customer Service
Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
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.- Anthology ID:
- 2021.findings-emnlp.24
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 245–260
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.24
- DOI:
- 10.18653/v1/2021.findings-emnlp.24
- Cite (ACL):
- Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, and Ranit Aharonov. 2021. TWEETSUMM - A Dialog Summarization Dataset for Customer Service. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 245–260, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- TWEETSUMM - A Dialog Summarization Dataset for Customer Service (Feigenblat et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.24.pdf