Low-resource extractive text summarization is a vital but heavily underexplored area of research. Prior literature either focuses on abstractive text summarization or prompts a large language model (LLM) like GPT-3 directly to generate summaries. In this work, we propose MixSumm for low-resource extractive text summarization. Specifically, MixSumm prompts an open-source LLM, LLaMA-3-70b, to generate documents that mix information from multiple topics as opposed to generating documents without mixup, and then trains a summarization model on the generated dataset. We use ROUGE scores and L-Eval, a reference-free LLaMA-3-based evaluation method to measure the quality of generated summaries. We conduct extensive experiments on a challenging text summarization benchmark comprising the TweetSumm, WikiHow, and ArXiv/PubMed datasets and show that our LLM-based data augmentation framework outperforms recent prompt-based approaches for low-resource extractive summarization. Additionally, our results also demonstrate effective knowledge distillation from LLaMA-3-70b to a small BERT-based extractive summarizer.

在本研究中，我们提出了一种名为MixSumm的低资源提取式文本摘要方法，该方法在开源语言模型LLaMA-3-70b上生成多主题混合文档，并在生成的数据集上训练一个摘要模型。我们使用ROUGE分数和L-Eval作为评估方法，通过在TweetSumm、WikiHow和ArXiv/PubMed数据集上进行的广泛实验证明，我们的LLM数据增强框架在低资源提取式摘要方面优于最近的基于提示的方法。此外，我们的结果还展示了从LLaMA-3-70b到小型BERT提取式摘要器的有效知识蒸馏。

MixSumm: 低资源抽取式文本摘要的基于主题的数据增强利用LLMs