Fast Rhetorical Structure Theory Discourse Parsing
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Abstract (arXiv)
In recent years, There has been a variety of research on discourse parsing, particularly RST discourse parsing. Most of the recent work on RST parsing has focused on implementing new types of features or learning algorithms in order to improve accuracy, with relatively little focus on efficiency, robustness, or practical use. Also, most implementations are not widely available. Here, we describe an RST segmentation and parsing system that adapts models and feature sets from various previous work, as described below. Its accuracy is near state-of-the-art, and it was developed to be fast, robust, and practical. For example, it can process short documents such as news articles or essays in less than a second.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1.
4 papers.
- 2021DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsingcited by 27
- 2024Personalized Text Generation with Fine-Grained Linguistic Controlcited by 20
- 2021Persian Rhetorical Structure Theorycited by 6
- 2022A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsingcited by 4
- 2024Personalized Text Generation with Fine-Grained Linguistic Control7 Feb 2024
- 2022A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing15 Oct 2022
- 2021DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing9 Oct 2021
- 2021Persian Rhetorical Structure Theory25 Jun 2021