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

Fuente: arXiv
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Main Authors: Guo, Xiaobo, Vosoughi, Soroush
Format: Preprint
Published: 2024
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author Guo, Xiaobo
Vosoughi, Soroush
author_facet Guo, Xiaobo
Vosoughi, Soroush
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2406_03479
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization
Guo, Xiaobo
Vosoughi, Soroush
Computation and Language
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.
title MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization
topic Computation and Language
url https://arxiv.org/abs/2406.03479