1.	The paper lacks a clear motivation for considering text graphs. Except for the choice of complexity indices, which can easily be changed according to the domain, the proposed method is general and can be applied to other graphs or even other types of data. Moreover, formal formulations of text graphs and the research question are missing in the paper.
2.	Several curriculum learning methods have been discussed in Section 1. However, the need for designing a new curriculum learning method for text graphs is not justified. The research gap, e.g., why existing methods can’t be applied, is not discussed.
3.	Equations 7-11 provide several choices of function f. However, there is no theoretical analysis or empirical experiments to advise on the choice of function f.
4.	In the overall performance comparison (Table 2), other curriculum learning methods do not improve performance compared to No-CL. These results are not consistent with the results reported in the papers of the competitors. At least some discussions about the reason should be included. In addition, it is unclear how many independent runs have been conducted to get the accuracy and F1. What are the standard deviations?
5.	Although experimental results in Table 3 and Table 4 show that the performance remains unchanged, it is unclear how the transfer of knowledge is done in the proposed method. An in-depth discussion of this property should strengthen the soundness of the paper.
6.	In line 118, the authors said the learned curricula are model-dependent, but they also said the curricula are transferrable across models. These two statements seem to be contradictory.
2. Several curriculum learning methods have been discussed in Section 1. However, the need for designing a new curriculum learning method for text graphs is not justified. The research gap, e.g., why existing methods can’t be applied, is not discussed.