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TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

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arxiv 2111.11894 v1 pith:GYKXYZM4 submitted 2021-11-23 cs.CL

Guy Feigenblat , Chulaka Gunasekara , Benjamin Sznajder , Sachindra Joshi , David Konopnicki , Ranit Aharonov This is my paper · ORCID
classification cs.CL
keywords customeragentssummarizationdatasetdialogdialogsextractivehuman
verification ladder T0 review T1 audit T2 compute T3 formal
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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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    cs.CL 2024-12 conditional novelty 4.0 of 10

    Adding LLM-generated social context to excerpts from long social conversations improves readers' rated comprehension and empathy, but the models still miss nuanced social details.

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