Text segmentation on multilabel documents: A distant-supervised approach
TABLE II: Results on the segmentation and classification tasks.
['Dataset', 'Model', '\\operatornamewithlimits  [ITALIC] SOV', '\\operatornamewithlimits  [ITALIC] PPPA', '\\operatornamewithlimits  [ITALIC] F1 [ITALIC] mean', '\\operatornamewithlimits  [ITALIC] AUCμ', '\\operatornamewithlimits  [ITALIC] AUCM']
Except for the Ohsumed dataset, SEG-REFINE performs better than the SEG-NOISY. On the Ohsumed dataset, SEG-REFINE performs at par with SEG-NOISY. The average performance gain of SEG-REFINE over SEG-NOISY over all the datasets, in terms of \operatornamewithlimitsSOV metric is 4.5%. This illustrates that the segmentation can give us more precise examples of text segments belonging to a class, leading to better classification models. SEG-NOISY and SEG-REFINE perform similarly on all the three metrics. This indicates that SEG-NOISY is able to predict the labels in a document as well as SEG-REFINE. However, the locations of these predicted labels are not as precise as predicted by SEG-REFINE, which leads to at par performance on the multilabel classification task but lower performance on the segmentation task.