@inproceedings{bannihatti-kumar-etal-2021-supportnet,
title = "{S}upport{N}et: Neural Networks for Summary Generation and Key Segment Extraction from Technical Support Tickets",
author = "Bannihatti Kumar, Vinayshekhar and
Yarramsetty, Mohan and
Sun, Sharon and
Goel, Anukul",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 4th Workshop on e-Commerce and NLP",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.ecnlp-1.20",
doi = "10.18653/v1/2021.ecnlp-1.20",
pages = "164--173",
abstract = "We improve customer experience and gain their trust when their issues are resolved rapidly with less friction. Existing work has focused on reducing the overall case resolution time by binning a case into predefined categories and routing it to the desired support engineer. However, the actions taken by the engineer during case analysis and resolution are altogether ignored, even though it forms the bulk of the case resolution time. In this work, we propose two systems that enable support engineers to resolve cases faster. The first, a guidance extraction model, mines historical cases and provides technical guidance phrases to the support engineers. The phrases can then be used to educate the customer or to obtain critical information needed to resolve the case and thus minimize the number of correspondences between the engineer and customer. The second, a summarization model, creates an abstractive summary of the case to provide better context to the support engineer. Through quantitative evaluation we obtain an F1 score of 0.64 on the guidance extraction model and a BertScore (F1) of 0.55 on the summarization model.",
}
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%0 Conference Proceedings
%T SupportNet: Neural Networks for Summary Generation and Key Segment Extraction from Technical Support Tickets
%A Bannihatti Kumar, Vinayshekhar
%A Yarramsetty, Mohan
%A Sun, Sharon
%A Goel, Anukul
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 4th Workshop on e-Commerce and NLP
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F bannihatti-kumar-etal-2021-supportnet
%X We improve customer experience and gain their trust when their issues are resolved rapidly with less friction. Existing work has focused on reducing the overall case resolution time by binning a case into predefined categories and routing it to the desired support engineer. However, the actions taken by the engineer during case analysis and resolution are altogether ignored, even though it forms the bulk of the case resolution time. In this work, we propose two systems that enable support engineers to resolve cases faster. The first, a guidance extraction model, mines historical cases and provides technical guidance phrases to the support engineers. The phrases can then be used to educate the customer or to obtain critical information needed to resolve the case and thus minimize the number of correspondences between the engineer and customer. The second, a summarization model, creates an abstractive summary of the case to provide better context to the support engineer. Through quantitative evaluation we obtain an F1 score of 0.64 on the guidance extraction model and a BertScore (F1) of 0.55 on the summarization model.
%R 10.18653/v1/2021.ecnlp-1.20
%U https://aclanthology.org/2021.ecnlp-1.20
%U https://doi.org/10.18653/v1/2021.ecnlp-1.20
%P 164-173
Markdown (Informal)
[SupportNet: Neural Networks for Summary Generation and Key Segment Extraction from Technical Support Tickets](https://aclanthology.org/2021.ecnlp-1.20) (Bannihatti Kumar et al., ECNLP 2021)
ACL