A Comparative Review of Long Short-Term Memory Based Encoder-Decoder, Transformer, and Hybrid Architectures for Text Sum-marization

Abstract

Natural language processing is a recent phenomenon that has been growing in recent years owing to the availa-bility of text documents online. With these massive repositories of text information, researchers have focused on automat-ically and efficiently extracting information by using text summarization systems. Their application is found in so-cial network analysis, market intelligence, educational systems, and financial analysis. In this paper, the recent progress in automatic summarization is comprehensively described. This paper concerns the different optimization methods in text summarization. We present a taxonomy that illustrates the use of different strategies in automatic text summarization in situations where there is single or multi-document automatic text summarization. We present the summaries of some representative state-of-the-art automated text summarization methods and compare them in benchmark datasets. Possible applications, research problems, open issues, and future directions of the automatic text summarization sphere are also addressed in the paper.Extractive text summarization techniques are gaining in-creased popularity because they are capable of generating an informative summary with minimal linguistic re-sources. The next step after converting a sentence to a bag of words will be to first select important sentences in the text, which we shall summarize. Due to the strong data dependence on the performance of the methods, the effect of the parameter on different datasets is examined. Furthermore, we also consider the methods of assessment such as ROUGE metrics and more recent semantic-based ones to assess quality of the summary and the competitiveness of the facts.