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arXiv · 2305.16784

Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization

Dongqi Liu·Yifan Wang·Vera Demberg
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Abstract

For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory (RST) into transformer-based summarization models only consider the nuclearity annotation, thereby overlooking the variety of discourse relation types. This paper introduces the 'RSTformer', a novel summarization model that comprehensively incorporates both the types and uncertainty of rhetorical relations. Our RST-attention mechanism, rooted in document-level rhetorical structure, is an extension of the recently devised Longformer framework. Through rigorous evaluation, the model proposed herein exhibits significant superiority over state-of-the-art models, as evidenced by its notable performance on several automatic metrics and human evaluation.

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Dongqi Liu, Yifan Wang, Vera Demberg. 2023-05-26. Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization. https://arxiv.org/abs/2305.16784

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