Dongqi Pu

My name in Oracle bone script:

Doctoral researcher in Computational Linguistics

Campus C7.4, Saarland University, 66123, Germany [AT]

Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization


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.



Code is available at:


    title = "Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization",
    author = "Pu, Dongqi and Wang, Yifan and Demberg, Vera",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "5574--5590"}