ReFeed: Multi-dimensional Summarization Refinement with Reflective
  Reasoning on Feedback
Authors: name: Taewon Yun, name: Jihwan Oh, name: Hyangsuk Min, name: Yuho Lee, name: Jihwan Bang, name: Jason Cai, name: Hwanjun Song

Summarization refinement faces challenges when extending to multi-dimension.
In this paper, we introduce ReFeed, a powerful summarization refinement
pipeline that enhances multiple dimensions through reflective reasoning on
feedback. To achieve this, we release SumFeed-CoT, a large-scale Long-CoT-based
dataset optimized for training a lightweight model with reflective reasoning.
Our experiments reveal how the number of dimensions, feedback exposure, and
reasoning policy influence refinement performance, highlighting reflective
reasoning and simultaneously addressing multiple feedback is crucial to
mitigate trade-off between dimensions. Furthermore, ReFeed is robust to noisy
feedback and feedback order. Lastly, our finding emphasizes that creating data
with a proper goal and guideline constitutes a fundamental pillar of effective
reasoning. The dataset and model will be released.