Disclosed is an abstractive summarization method for summarizing documents, including long documents. The method for generating a summarization of a structured document having a plurality of sections comprises: processing each word in a plurality of words in a section using a respective first recurrent neural network to generate a word-level representation; processing each word-level representation by a second recurrent neural network to generate a section-level representation; generating a context vector by performing a neural attention process on one or more hidden states of said first recurrent neural network and one or more hidden stats of said second recurrent neural network; and then generating a next predicted word in said summarization based upon a previously predicted work and said context vector. The method is used for example to generate a document S from scratch by using words and phrases that are not exactly from the original document D. This robust abstractive summarization is a generally more natural way to summarize a document as humans also tend to write abstractive summaries in their own words rather than copying exact sentences from a source document. This method is particularly useful for summarizing long complex documents.
Abstractive summarization of long documents using deep learning