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

TL;DR

This paper introduces Baichuan2-Sum, an instruction fine-tuned large language model designed for role-oriented dialogue summarization, achieving state-of-the-art results on public datasets.

Contribution

The paper presents a novel instruction fine-tuning approach for large models in dialogue summarization, incorporating role-specific instructions and NEFTune noise techniques.

Findings

Achieves new state-of-the-art results on CSDS and SAMSUM datasets.

Demonstrates effectiveness of role-specific instructions in dialogue summarization.

Provides open-source model and code for future research.

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 conduct 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 diaglouge summarization. By setting different instructions for different roles, the model can learn from the dialogue interactions and output the expected summaries. Furthermore, we applied NEFTune technique to add suitable noise during training to improve 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. We release our model and related codes to facilitate future studies on dialogue summarization task.