  The evaluation of summary quality encompasses diverse dimensions such as
consistency, coherence, relevance, and fluency. However, existing summarization
methods often target a specific dimension, facing challenges in generating
well-balanced summaries across multiple dimensions. In this paper, we propose
multi-objective reinforcement learning tailored to generate balanced summaries
across all four dimensions. We introduce two multi-dimensional optimization
(MDO) strategies for adaptive learning: 1) MDO_min, rewarding the current
lowest dimension score, and 2) MDO_pro, optimizing multiple dimensions similar
to multi-task learning, resolves conflicting gradients across dimensions
through gradient projection. Unlike prior ROUGE-based rewards relying on
reference summaries, we use a QA-based reward model that aligns with human
preferences. Further, we discover the capability to regulate the length of
summaries by adjusting the discount factor, seeking the generation of concise
yet informative summaries that encapsulate crucial points. Our approach
achieved substantial performance gains compared to baseline models on
representative summarization datasets, particularly in the overlooked
dimensions.
