From News to Medical: Cross-domain Discourse Segmentation
Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded).
['RST Seg', 'Domain', 'F1', 'P', 'R']
As expected, the News domain outperforms the Medical domain, regardless of which segmenter is used. In the case of the DPLP segmenter, the gap between the two domains is about 7.4 F1 points. Note that the performance of DPLP on News lags considerably behind the state of the art (-14.76 F1 points). When switching to the two-pass segmenter, the performance on News increases dramatically (+13 F1 points). However, the performance on Medical increases by only 3.75 F1 points. Thus, large gains in News translate into only a small gain in Medical. The neural segmenter achieves the best performance on News and is also able to more successfully close the gap on Medical, with only a 5.64 F1 difference, largely attributable to lower recall.