Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization

Abstract

Large language models (LLMs) like LLaMA, Baichuan and Bloom models show remarkable ability with instruction fine-tuning in many natural language tasks. Nevertheless, for the dialogue summarization task, which aims to generate summaries for different roles in dialogue, most of the state-of-the-art methods foucus on small models (e.g BART and BERT). Existing methods try to add task specified optimization on small models like adding global-local centrality score to models. In this paper, we propose an instruction fine-tuning model: Baichuan2-Sum, for role-oriented dialogue summarization. By setting different instructions for different roles, the model can learn from the dialogue interactions and output the desired summaries. Furthermore, we applied NEFTune technique to add suitable noise during training, improving the results. The experiments demonstrate that the proposed model achieves the new state-of-the-art results on two public dialogue summarization datasets: CSDS and SAMSUM. The Baichuan2-Sum model shows an improvement in Rouge scores on both datasets compared to the previously best-performing model. Notably, for the SAMSUM dataset, there is a 21% increase in the ROUGE-1 score, a 32% increase in the ROUGE-2 score, and a 9% increase in the ROUGE-L score. We have released our model and related codes to facilitate future studies in the dialogue summarization task.