  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.
