At the beginning, when the segmentation prediction network performs poorly, the model pools over only ground truth segment bounds, allowing it to learn the cleanest topic representations.
as training progresses and the segmentation accuracy begins to converge, we switch from pooling over ground truth segments to aligning predicted and ground truth segment.