  The rapid proliferation of online content necessitates effective
summarization methods, among which dynamic aspect-based summarization stands
out. Unlike its traditional counterpart, which assumes a fixed set of known
aspects, this approach adapts to the varied aspects of the input text. We
introduce a novel multi-objective learning framework employing a
Longformer-Encoder-Decoder for this task. The framework optimizes aspect number
prediction, minimizes disparity between generated and reference summaries for
each aspect, and maximizes dissimilarity across aspect-specific summaries.
Extensive experiments show our method significantly outperforms baselines on
three diverse datasets, largely due to the effective alignment of generated and
reference aspect counts without sacrificing single-aspect summarization
quality.
