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Currently making a bot that condenses news articles. I'm tagging sentences as important or not important using a simple BERT classifier. The results were... not great. I'm really interested in how I can improve the results using LSTM.

I'm now batching 5 sentences together, calculating their BERT encodings, and then using 2 LSTM Layers, one backwards one forwards, to predict if the sentence is important.

Unfortunately I'm now calculating 5 times the number of embeddings, and if it doesn't work, I can't seem to figure out how to feed a variable number of things into BERT using Tensorflow, to see if I can tweak some results.

Are there other methods to add surrounding sentences to this context?

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You're trying to do Extractive Text Summarization.

You're first approach is the right one, using BERT + CLassifier.

From NLP Progress, the best method so far is Presumm.

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  • $\begingroup$ Just to clarify, are you saying I shouldn't use the LSTM on sentence outputs of BERT? I'm also interested in maybe making a few versions of an output. I.e. very condensed, somewhat condensed, etc. Presumm seems to have some of this, but if I want to have more values, is there something similar for a multi-class classifier? $\endgroup$ – bbbbbb Nov 7 '19 at 22:05
  • $\begingroup$ What you can do (if you have the adequate data for such a training) is simply training several classifiers. BERT will be shared among all classifiers and later you can choose which classifier you want (very condensed, somewhat condensed, etc...) $\endgroup$ – Astariul Nov 7 '19 at 23:31

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