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Hyper-parameters. For AON model we use the code base provided by the authors in (Cui et al., 2018) and we maintain the hyper-parameters described in the paper. For the paragraph encoder of the B-AON models, we follow the same scheme of the AON model but for its sentence encoder we use hyper-parameters of the BERT setting. We use the pretrained BERT uncased base model with 12 layers for the B-AON and B-TSORT models. We fine-tune the BERT model in both cases. Hence, we replace the Adadelta optimizer with the BertAdam (Wolf et al., 2019) optimizer for the B-AON model. The LSTMs in the L-TSort model uses an RNN size of 512 and it uses the same vocabularies as the AON model. L-TSort is trained using stochastic gradient descent with dropout of 0.2, learning rate of 1.0 and learning decay rate of 0.5. For B-TSort and L-TSort we use accuracy on the validation set to stop training. For B-TSort and B-AON we use learning rate of 5e-5 with adam epsilon value of 1e-8. For all the experiments we use a maximum sequence length of 105 tokens.
Gold Order	B-TSort Order	B-AON Order
SIND Dataset
the family sits together for dinner on the first night of the annual re-union. the restaurant we chose had amazing food and everyone loved the presentation. gemma really adored the restaurants decorations and was always gazing at them. aunt harriot had a little trouble deciding what kind of wine she wanted tonight. bob had the whole family cracking up with his jokes.	the family sits together for dinner on the first night of the annual re-union. the restaurant we chose had amazing food and everyone loved the presentation. aunt harriot had a little trouble deciding what kind of wine she wanted tonight. gemma really adored the restaurants decorations and was always gazing at them. bob had the whole family cracking up with his jokes.	the family sits together for dinner on the first night of the annual re-union. aunt harriot had a little trou-ble deciding what kind of wine she wanted tonight. bob had the whole family cracking up with his jokes. gemma really adored the restau- rants decorations and was always gazing at them. the restaurant we chose had amazing food and every- one loved the presentation.
he wanted to take a ride on his new bike. we went on a nice ride out to the lake. we really enjoyed the beautiful view from the dock. it was very peaceful watching the boats. we had such a busy day he needed a nap.	we went on a nice ride out to the lake. he wanted to take a ride on his new bike. we really enjoyed the beautiful view from the dock. it was very peaceful watching the boats. we had such a busy day he needed a nap.	we went on a nice ride out to the lake. he wanted to take a ride on his new bike. it was very peaceful watching the boats. we really en-joyed the beautiful view from the dock. we had such a busy day he needed a nap.
when we finally brought our son home from the hospital so many people were at home with us to see him. everyone wanted a chance to hold him! we were all so happy to have a new addition to the family. my parents were so proud to be grand parents! i am so happy and i love my son very much!	when we finally brought our son home from the hospital so many people were at home with us to see him. we were all so happy to have a new addition to the family. every- one wanted a chance to hold him! my parents were so proud to be grand parents! i am so happy and i love my son very much!	my parents were so proud to be grand parents! when we finally brought our son home from the hospital so many people were at home with us to see him. we were all so happy to have a new addition to the family. everyone wanted a chance to hold him! i am so happy and i love my son very much!
Table 6: Examples of predicted sentence orders for B-TSort and B-AON model for SIND dataset.
Table 7: Examples of predicted sentence orders for B-TSort and B-AON model for NIPS dataset.