This repository showcases fine-tuning the Google/Pegasus model for customer conversation summarization. The original model checkpoint was trained on the CNN/Daily Mail dataset. We further fine-tune the model on the SAMSum dataset, developed by Samsung, which consists of a collection of dialogues along with brief summaries.
Customer conversations play a crucial role in an enterprise setting, and summarizing these conversations can provide valuable insights and save time for both customers and businesses. With the help of advanced natural language processing models like Pegasus, we can automate the process of summarizing customer conversations, enabling businesses to quickly extract key information and identify important trends.
The Google/Pegasus model is a state-of-the-art sequence-to-sequence model that excels at abstractive text summarization. By fine-tuning this model on the SAMSum dataset, we have specifically tailored it to summarize customer conversations. The model learns to generate concise summaries while capturing the essential information from the dialogues.
The SAMSum dataset contains a diverse collection of dialogues from various domains, making it suitable for training a model for customer conversation summarization. Each dialogue in the dataset is accompanied by a brief summary, providing a reference for the model during training. This dataset enables the model to learn the nuances of customer conversations and generate accurate and informative summaries.