Textsum - Incorrect decode results compared to ref file - tensorflow

Question

This issue is seen when performing training against my own dataset which was converted to binary via data_convert_example.py. After a week of training I get decode results that don't make sense when comparing the decode and ref files.
If anyone has been successful and gotten results similar to what is posted in the Textsum readme using their own data, I would love to know what has worked for you...environment, tf build, number of articles.
I currently have not had luck with 0.11, but have gotten some results with 0.9 however the decode results are similar to those shown below which I have no idea where they are even coming from.
I currently am running Ubuntu 16.04, TF 0.9, CUDA 7.5 and CuDnn 4. I tried TF 0.11 but was dealing with other issues so I went back to 0.9. It does seem that the decode results are being generated from valid articles, but the reference file and decode file indicies have NO correlation.
If anyone can provide any help or direction, it would be greatly appreciated. Otherwise, should I figure anything out, I will post here.
A few final questions. Regarding the vocab file referenced. Does it at all need to be sorted by word frequency at all? I never performed anything along these lines when generating it and just wasn't sure if this would throw something off as well.
Finally, I made the assumption in generating the data that the training data articles should be broken down into smaller batches. I separated out the articles into multiple files of 100 articles each. These were then named data-0, data-1, etc. I assume this was a correct assumption on my part? I also kept all the vocab in one file which has not seemed to throw any errors.
Are the above assumptions correct as well?
Below are some ref and decode results which you can see are quite odd and seem to have no correlation.
DECODE:
output=Wild Boy Goes About How I Can't Be Really Go For Love
output=State Department defends the campaign of Iran
output=John Deere sails profit - Business Insider
output=to roll for the Perseid meteor shower
output=Man in New York City in Germany
REFERENCE:
output=Battle Chasers: Nightwar Combines Joe Mad's Stellar Art With Solid RPG Gameplay
output=Obama Meets a Goal That Could Literally Destroy America
output=WOW! 10 stunning photos of presidents daughter Zahra Buhari
output=Koko the gorilla jams out on bass with Flea from Red Hot Chili Peppers
output=Brenham police officer refused service at McDonald's

Answer 1

Going to answer this one myself. Seems the issue here was the lack of training data. In the end I did end up sorting my vocab file, however it seems this is not necessary. The reason this was done, was to allow the end user to limit the vocab words to something like 200k words should they wish.
The biggest reason for the problems above were simply the lack of data. When I ran the training in the original post, I was working with 40k+ articles. I thought this was enough but clearly it wasn't and this was even more evident when I got deeper into the code and gained a better understanding as to what was going on. In the end I increased the number of articles to over 1.3 million, I trained for about a week and a half on my 980GTX and got the average loss to about 1.6 to 2.2 I was seeing MUCH better results.
I am learning this as I go, but I stopped at the above average loss because some reading I performed stated that when you perform "eval" against your "test" data, your average loss should be close to what you are seeing in training. This helps to determine whether you are getting close to over-fitting when these are far apart. Again take this with a grain of salt, as I am learning but it seems to make sense logically to me.
One last note that I learned the hard way is this. Make sure you upgrade to the latest 0.11 Tensorflow version. I originally trained using 0.9 but when I went to figure out how to export the model for tensorflow, I found that there was no export.py file in that repo. When I upgrades to 0.11, I then found that the checkpoint file structure seems to have changed in 0.11 and I needed to take another 2 weeks to train. So I would recommend just upgrading as they have resolved a number of the problems I was seeing during the RC. I still did have to set the is_tuple=false but that aside, all has worked out well. Hope this helps someone.