Text summarization is the problem of reducing the number of sentences and words of a document without changing its meaning.. There are different techniques to extract information from raw text data and use it for a summarization model, overall they can be categorized as Extractive and Abstractive.
special class of Recurrent Neural Network architectures that we typically use (but not restricted) to solve complex Language problems like Machine Translation, Question Answering, creating Chatbots, Text Summarization, etc.. The most common architecture used to build Seq2Seq models is Encoder�Decoder architecture. Workflow: Input:Abstract->Model->Output:Research Highlights
1)Starting with some Data Analysis which is needed for the next Feature Engineering. 2)Next we can have a look at the length distribution by counting the words 3)After that, we have all we need to proceed with Feature Engineering. The feature matrix is created by transforming the preprocessed corpus into a list of sequences using TensorFlow/Keras 4)Next we take care of the summaries. Before applying the same feature engineering strategy we need to add two special tokens inside each summary that determine the beginning and end of the text. 5)Then we build the encoder-decoder model and apply a Seq2Seq algorithm to get the summary.