Abstractive Text Summarization Using Attention-based Stacked LSTM

Abstract

Every day, the amount of textual data created increases exponentially, both in terms of complexity and volume. Massive amounts of information are generated by social media, news articles, emails, text messages and other resources, making it difficult to read lengthy language materials. Our main objective in the paper is to obtain a short understandable and fluent abstractive summary of any given text. The Abstractive Text Summarizer automatically gives the summary of the text by generating new phrase, rephrasing or using the new words which are not present in the original text. In this paper, a machine learning architecture i.e. Stacked LSTM based on attention mechanism using Sequence-to-Sequence model is proposed, to generate the summary using abstractive approach for Amazon reviews of fine foods dataset. Our approach allows the model to accept content and provide a concise summary that may clearly describe the gist of the original text. The experiments on Amazon reviews of fine foods dataset show that our model obtained BLEU Score as 0.91 for a test set.