@inproceedings{feigenblat-etal-2021-tweetsumm-dialog,
title = "{TWEETSUMM} - A Dialog Summarization Dataset for Customer Service",
author = "Feigenblat, Guy and
Gunasekara, Chulaka and
Sznajder, Benjamin and
Joshi, Sachindra and
Konopnicki, David and
Aharonov, Ranit",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.24",
doi = "10.18653/v1/2021.findings-emnlp.24",
pages = "245--260",
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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<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.</abstract>
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%0 Conference Proceedings
%T TWEETSUMM - A Dialog Summarization Dataset for Customer Service
%A Feigenblat, Guy
%A Gunasekara, Chulaka
%A Sznajder, Benjamin
%A Joshi, Sachindra
%A Konopnicki, David
%A Aharonov, Ranit
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F feigenblat-etal-2021-tweetsumm-dialog
%X 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.
%R 10.18653/v1/2021.findings-emnlp.24
%U https://aclanthology.org/2021.findings-emnlp.24
%U https://doi.org/10.18653/v1/2021.findings-emnlp.24
%P 245-260
Markdown (Informal)
[TWEETSUMM - A Dialog Summarization Dataset for Customer Service](https://aclanthology.org/2021.findings-emnlp.24) (Feigenblat et al., Findings 2021)
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.