1. The paper need further polish to make readers easy to follow.
2. In Table 6, the improvement of method is marginal and unstable.
3. The motivation of this new task is not strong enough to convince the reader. Is it a necessary intermediate task for document summarization and text mining (as stated in L261)?
4. It directly reverse the table-to-text settings then conducts the experiments on four existing table-to-text datasets. More analysis of the involved datasets is required, such as the number of output tables, the size/schema of the output tables.
Questions: 1. L68-L70, Is there any further explanation of the statement "the schemas for extraction are implicitly included in the training data"?
2. How to generate the table content that not shown in the text?
3. Why not merge Table 1 and Table 2? They are both about the statistics of datasets used in experiments.
4. What’s the relation between text-to-table task and vanilla summarization task?
5. How to determine the number of output table(s)? Appendex. C don't provide an answer about this.
6. What’s the version of BART in Table3 and Table 4?
Suggestions: 1. The font size of Figure 2 and Figure 3 is too small.
Typos: 1. L237: Text-to-table -> text-to-table 2. L432: "No baseline can be applies to all four datasets" is confusing.
3. Table 3: lOur method -> Our method
2. How to generate the table content that not shown in the text?