Fast Rhetorical Structure Theory Discourse Parsing

TL;DR

This paper presents a fast, robust, and practical RST discourse parsing system that achieves near state-of-the-art accuracy and can process short documents in under a second.

Contribution

It introduces an efficient RST parsing system that emphasizes speed and practicality while maintaining high accuracy, addressing gaps in previous work focused mainly on accuracy improvements.

Findings

Achieves near state-of-the-art accuracy

Processes short documents in less than a second

Focuses on efficiency and robustness

Abstract

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.