# RST-style Discourse Parsing Guided by Document-level Content Structures

arXiv: https://arxiv.org/abs/2309.04141 · HTML: https://mlsanity.com/paper/2309.04141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Cross-lingual RST Discourse Parsing](https://mlsanity.com/paper/1701.02946/) (2017)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure](https://mlsanity.com/paper/2005.02680/) (2020)
- [Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking](https://mlsanity.com/paper/1906.05691/) (2019)
- [Top-down Discourse Parsing via Sequence Labelling](https://mlsanity.com/paper/2102.02080/) (2021)
- [Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees](https://mlsanity.com/paper/1704.05228/) (2017)
- [Neural Generative Rhetorical Structure Parsing](https://mlsanity.com/paper/1909.11049/) (2019)
- [Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsing](https://mlsanity.com/paper/2210.09537/) (2022)

## Built on by (most cited first, 1 of 1)

- [Semi-supervised News Discourse Profiling with Contrastive Learning](https://mlsanity.com/paper/2309.11692/) (2023)

## Abstract (arXiv)

Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how clauses, sentences, and large text spans compose a whole discourse and presents the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines construct rhetorical structures without the knowledge of document-level content structures, which causes relatively low performance when predicting the discourse relations for large text spans. Recognizing the value of high-level content-related information in facilitating discourse relation recognition, we propose a novel pipeline for RST-DP that incorporates structure-aware news content sentence representations derived from the task of News Discourse Profiling. By incorporating only a few additional layers, this enhanced pipeline exhibits promising performance across various RST parsing metrics.