Generating high-quality summaries for chat dialogs often requires large
labeled datasets. We propose a method to efficiently use unlabeled data for
extractive summarization of customer-agent dialogs. In our method, we frame
summarization as a question-answering problem and use state-of-the-art large
language models (LLMs) to generate pseudo-labels for a dialog. We then use
these pseudo-labels to fine-tune a chat summarization model, effectively
transferring knowledge from the large LLM into a smaller specialized model. We
demonstrate our method on the \tweetsumm dataset, and show that using 10\% of
the original labelled data set we can achieve 65.9/57.0/61.0 ROUGE-1/-2/-L,
whereas the current state-of-the-art trained on the entire training data set
obtains 65.16/55.81/64.37 ROUGE-1/-2/-L. In other words, in the worst case
(i.e., ROUGE-L) we still effectively retain 94.7% of the performance while
using only 10% of the data.

使用未标记数据进行客户 - 代理商对话的摘要生成，通过将摘要问题建模为问答问题，使用大型语言模型生成伪标签，并通过精细调整专门的聊天总结模型来有效地从大型语言模型中转移知识。