  Automatic summarization methods are efficient but can suffer from low
quality. In comparison, manual summarization is expensive but produces higher
quality. Can humans and AI collaborate to improve summarization performance? In
similar text generation tasks (e.g., machine translation), human-AI
collaboration in the form of "post-editing" AI-generated text reduces human
workload and improves the quality of AI output. Therefore, we explored whether
post-editing offers advantages in text summarization. Specifically, we
conducted an experiment with 72 participants, comparing post-editing provided
summaries with manual summarization for summary quality, human efficiency, and
user experience on formal (XSum news) and informal (Reddit posts) text. This
study sheds valuable insights on when post-editing is useful for text
summarization: it helped in some cases (e.g., when participants lacked domain
knowledge) but not in others (e.g., when provided summaries include inaccurate
information). Participants' different editing strategies and needs for
assistance offer implications for future human-AI summarization systems.
