An End-to-End Dialogue Summarization System for Sales Calls
Abedelkadir Asi, Song Wang, Roy Eisenstadt, Dean Geckt, Yarin Kuper, Yi Mao, Royi Ronen
Abstract
Summarizing sales calls is a routine task performed manually by salespeople. We present a production system which combines generative models fine-tuned for customer-agent setting, with a human-in-the-loop user experience for an interactive summary curation process. We address challenging aspects of dialogue summarization task in a real-world setting including long input dialogues, content validation, lack of labeled data and quality evaluation. We show how GPT-3 can be leveraged as an offline data labeler to handle training data scarcity and accommodate privacy constraints in an industrial setting. Experiments show significant improvements by our models in tackling the summarization and content validation tasks on public datasets.
BibTeX
@inproceedings{asi-etal-2022-end,
title = "An End-to-End Dialogue Summarization System for Sales Calls",
author = "Asi, Abedelkadir and
Wang, Song and
Eisenstadt, Roy and
Geckt, Dean and
Kuper, Yarin and
Mao, Yi and
Ronen, Royi",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.6/",
doi = "10.18653/v1/2022.naacl-industry.6",
pages = "45--53"
}