# From News to Medical: Cross-domain Discourse Segmentation

arXiv: https://arxiv.org/abs/1904.06682 · HTML: https://mlsanity.com/paper/1904.06682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://mlsanity.com/paper/2302.06488/) (2023)
- [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021)

## Abstract (arXiv)

The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.