  Large Language Models (LLMs) have demonstrated impressive performance across
a wide range of NLP tasks, including summarization. Inherently LLMs produce
abstractive summaries, and the task of achieving extractive summaries through
LLMs still remains largely unexplored. To bridge this gap, in this work, we
propose a novel framework LaMSUM to generate extractive summaries through LLMs
for large user-generated text by leveraging voting algorithms. Our evaluation
on three popular open-source LLMs (Llama 3, Mixtral and Gemini) reveal that the
LaMSUM outperforms state-of-the-art extractive summarization methods. We
further attempt to provide the rationale behind the output summary produced by
LLMs. Overall, this is one of the early attempts to achieve extractive
summarization for large user-generated text by utilizing LLMs, and likely to
generate further interest in the community.
