Abstractive Text Summarization Using Recurrent Neural Networks: Systematic Literature Review

The easy access to technology has led to mass production of information. This has created a challenge among many users in selecting or picking the relevant information often deeply buried in the text that is being massively produced. Automatic text summarization is fraught with many challenges that are well known from the ROUGE benchmarking results compared to human summarization. Further, the output of the summarization is oftentimes syntactically wrong. Since the process uses corpus, the semantic result of the output is also a challenging task. The challenge is to identify which technique can improve the quality of summarised texts. This paper present results of the summarization techniques used for abstractive text summarization, by looking at different studies based on techniques, ROUGE scores, and datasets used. The paper concludes by identifying the gaps where further research is required.