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 realworld customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
| Original language | English |
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| Title of host publication | Findings of the Association for Computational Linguistics, Findings of ACL |
| Subtitle of host publication | EMNLP 2021 |
| Editors | Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 245-260 |
| Number of pages | 16 |
| ISBN (Electronic) | 9781955917100 |
| State | Published - 2021 |
| Externally published | Yes |
| Event | 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 - Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 |
Publication series
| Name | Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 |
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Conference
| Conference | 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 |
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| Country/Territory | Dominican Republic |
| City | Punta Cana |
| Period | 7/11/21 → 11/11/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics.