  Text summarization systems have made significant progress in recent years,
but typically generate summaries in one single step. However, the one-shot
summarization setting is sometimes inadequate, as the generated summary may
contain hallucinations or overlook essential details related to the reader's
interests. This paper addresses this limitation by proposing SummIt, an
iterative text summarization framework based on large language models like
ChatGPT. Our framework enables the model to refine the generated summary
iteratively through self-evaluation and feedback, resembling humans' iterative
process when drafting and revising summaries. Furthermore, we explore the
potential benefits of integrating knowledge and topic extractors into the
framework to enhance summary faithfulness and controllability. We automatically
evaluate the performance of our framework on three benchmark summarization
datasets. We also conduct a human evaluation to validate the effectiveness of
the iterative refinements and identify a potential issue of over-correction.
