MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization

• URL: http://arxiv.org/abs/2406.03479v2
• Date: Mon, 17 Jun 2024 19:56:37 GMT
• Title: MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization
• Authors: Xiaobo Guo, Soroush Vosoughi,
• Abstract summary: We introduce a novel multi-objective learning framework employing a Longformer-Encoder-Decoder for this task. We show our method significantly outperforms baselines on three diverse datasets.
• Score: 29.111115148808196
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. 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.

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