MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization
<|reference_start|>MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization: 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.<|reference_end|>
@article{guo2024modabs:,
    title={MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization},
    author={Xiaobo Guo, Soroush Vosoughi},
    journal={arXiv preprint arXiv:2406.03479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03479},
    primaryClass={cs.CL}
}