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For text summarization, the role of discourse structure is pivotal indiscerning the core content of a text. Regrettably, prior studies onincorporating Rhetorical Structure Theory (RST) into transformer-basedsummarization models only consider the nuclearity annotation, therebyoverlooking the variety of discourse relation types. This paper introduces the'RSTformer', a novel summarization model that comprehensively incorporates boththe types and uncertainty of rhetorical relations. Our RST-attention mechanism,rooted in document-level rhetorical structure, is an extension of the recentlydevised Longformer framework. Through rigorous evaluation, the model proposedherein exhibits significant superiority over state-of-the-art models, asevidenced by its notable performance on several automatic metrics and humanevaluation.

Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization