Removing junk sentences
I would suggest training your own custom Dialog Act Classification Model. Detecting a dialog act is a Natural Language Understanding (NLU) problem. Using a dialog act classification model you can detect if a sentence is used for "greeting", "question", "opinion", "promise", etc.

Detailed approach:

Use a pre-trained language model (for example BERT), to train your own Dialog act classification model. You can create your own custom Dialog act class based on your requirement (as you want to remove specific types of sentences). You can class them as junk or use "greeting", "question", "opinion", etc as dialog class.

After you are done with the training you can loop through the list of sentences and filter them based on their predicted classes. 


  You might have to research if there are similar corpus which you can
  use or have to manually label and then train the model. It will be a more reliable approach as compared to the word2vec or rule-based POS approach.


For more details on state-of-the-art approaches and dialogue act corpus follow this link. If you have no idea how to use a language model you can use the transformers library from huggingface. I hope you find this helpful.