June 6, 2024, 4:52 a.m. | Xiaobo Guo, Soroush Vosoughi

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.03479v1 Announce Type: new
Abstract: 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. …

abstract arxiv cs.cl dynamic framework input multi-objective novel online content set summarization text type

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