Sentiment-Based Abstractive Text Summarization Using Attention Oriented LSTM Model

Abstract

AbstractProduct reviews are essential as they help customers to make purchase decisions. Often these product reviews are used to be too abundant, lengthy and descriptive. However, the abstractive text summarization (ATS) system can help to build an internal semantic representation of the text with the use of natural language processing to create sentiment-based summaries of the entire reviews. An ATS system is proposed in this work, which is called a many-to-many sequence problem where attention-based long short-term memory (LSTM) is incorporated to generate the summary of the product reviews. The proposed ATS system works in two stages. In the first stage, a data processing module is designed to create the structured representation of the text, where noise and other irrelevant data are also removed. In the second stage, the attention-based long short-term memory (LSTM) model is designed to train, validate and test the system. It is found that the proposed approach can better deal with the rare words, also can generate readable, short and informative summaries. An experimental analysis has been carried out over the Amazon product review dataset, and to validate the result, the ROUGE measure is used. The obtained result reflects the efficacy of the proposed method over other state-of-the-art methods.KeywordsAbstractive text summarization (ATS)Attention mechanismLong short-term memory (LSTM)