LLM aided semi-supervision for efficient Extractive Dialog Summarization

Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu-Hanna, Issam H. Laradji

Submission Type: Regular Short Paper

Submission Track: Summarization

Submission Track 2: Efficient Methods for NLP

Keywords: Dialog Summarization, Large Language Models, Semi-supervised learning, Transformers, BART, GPT, Rouge

Abstract:

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.

Submission Number: 3865