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.

利用 Longformer-Encoder-Decoder 的新型多目标学习框架，实现了对在线内容进行动态方面的摘要，通过优化方面数量预测、减小生成与参考摘要之间的差异以及最大化方面特定摘要之间的差异来提升摘要质量。在广泛的实验中，我们的方法在三个多样的数据集上明显优于基线，而且在不损害单一方面摘要质量的情况下，成功对齐了生成和参考的方面数量。