  Large Language Models (LLMs) exhibit powerful summarization abilities.
However, their capabilities on conversational summarization remains under
explored. In this work we evaluate LLMs (approx. 10 billion parameters) on
conversational summarization and showcase their performance on various prompts.
We show that the summaries generated by models depend on the instructions and
the performance of LLMs vary with different instructions sometimes resulting
steep drop in ROUGE scores if prompts are not selected carefully. We also
evaluate the models with human evaluations and discuss the limitations of the
models on conversational summarization
