Fuzzy Bi-GRU Based Hybrid Extractive and Abstractive Text Summarization for Long Multi-documents

Abstract

As a massive amount of information is produced on the internet nowadays, the need for extracting the most useful and relevant information from that massive list is one of the most attractive research and it can be possible through a mechanism called automatic text summarization (ATS). This summarization mechanism is classified into single and multi-documents based on the number of source documents. When multiple source documents communicate similar information called multi-documents and it is the biggest challenge in the field of ATS. This motivates us to work on the long multi-documents by calculating the sentence scores using a fuzzy inference system. From the extracted sentences, the similarity or redundancy has to be removed using Bi- GRU, and then an abstractive summary need to be generated for those identified sentences has to produce. The proposed system is validated and tested using Standard datasets namely, DUC, BBC news, and CNN/daily mail. The proposed Fuzzy Bi-GRU is compared with other cutting-edge models, and empirical results indicate that it outperforms all other models in terms of ROUGE- N and L scores.