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This isn't completely clear to me. Say you have a massive passage that you want your LSTM to learn off of, how is it making sure it doesnt remove details from the first paragraph?

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I believe this paper will be of help. It explains the backpropagation algorithm.

Also note that for LSTM's that process passages, multiple LSTM blocks are used in a sequential and parallel manner. And additionally, neural networks are black boxes: we don't know how the work internally, and they make up which details are important themselves.

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