Abstractive Text Summarization Based on Improved LSTM with Transfer Learning Methods

Recently, there has been an increasing need for artificial text summarizing algorithms that attempt to automatically condense a document into a shorter form due to the rapid and exponential growth of textual data. As a result, automated text summarization is utilized by an extensive wide range of companies to help people find the most significant information. But still, there is a challenge in creating a brief and short summary of the original material that contains the key concepts. This paper intends to propose an abstractive text summarization based on LSTM with transfer learning methods. Initially, the long text-based document summarization process includes two phases. In the first phase, the input text is preprocessed for stop word removal and stemming techniques to reduce the document size. Then features like improved bag of words, TF-IDF, aspect term extraction and average word length-based features are extracted from the preprocessed text. In the second phase, the knowledge extraction has been performed. In the knowledge extraction, tokens are generated using BERT tokenization and the co-occurrence is utilized using improved co-occurrence matrix generation. The proposed method achieves the highest recall in 80% of learning which is 1.75% 1.39%, 2.58%, 1.19%, 0.30%, and 2.28% better than the other models such as Bi-GRU, RNN, LSTM, SVM-LR, CNN, LSTM-GRU and GRU respectively. Further, the knowledge extraction and the extracted features are subjected to the improved LSTM network, where the features are trained as the pre-trained model via transfer learning. This is the phase, where the final summary is produced.