Dongqi Pu

Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization

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.

Code:

Code is available at: https://github.com/dongqi-me/RSTformer

Citation:

@inproceedings{pu-etal-2023-incorporating,
    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 = "https://aclanthology.org/2023.acl-long.306",
    pages = "5574--5590"}