LLM aided semi-supervision for efficient Extractive Dialog Summarization
Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu–Hanna, Issam Laradji · 2023
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 customeragent 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 finetune 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 65.9/57.0/61.0ROUGE-1/-2/-L, whereas the current state-ofthe-art trained on the entire training data set obtains 65.16/55.81/64.37ROUGE-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.