Text summarization using deep learning techniques

Question

I am trying to summarize text documents that belong to legal domain.

I am referring to the site deeplearning.net on how to implement the deep learning architectures. I have read quite a few research papers on document summarization (both single document and multidocument) but I am unable to figure to how exactly the summary is generated for each document.

Once the training is done, the network stabilizes during testing phase. So even if I know the set of features (which I have figured out) that are learnt during the training phase, it would be difficult to find out the importance of each feature (because the weight vector of the network is stabilized) during the testing phase where I will be trying to generate summary for each document.

I tried to figure this out for a long time but it's in vain.

If anybody has worked on it or have any idea regarding the same, please give me some pointers. I really appreciate your help. Thank you.

Answer 1

I think you need to be a little more specific. When you say "I am unable to figure to how exactly the summary is generated for each document", do you mean that you don't know how to interpret the learned features, or don't you understand the algorithm? Also, "deep learning techniques" covers a very broad range of models - which one are you actually trying to use?

In the general case, deep learning models do not learn features that are humanly intepretable (albeit, you can of course try to look for correlations between the given inputs and the corresponding activations in the model). So, if that's what you're asking, there really is no good answer. If you're having difficulties understanding the model you're using, I can probably help you :-) Let me know.