Abstract:
This paper represents a comparative study and gives an overview on some of the great research work done on abstractive text summarization. Getting a gist part from a large text document is known as text summarization. In recent years, text summarization became one of the most interesting and crucial research point in Natural Language Processing(NLP) area. Text summarization is grouped in two subparts and those are Extractive Text Summarization(ETS) and Abstractive Text Summarization(ATS). ETS is more simple than ATS. ETS is based on algorithms and it extracts the salient words or sentences from the given large text document. As opposed to, ATS generates summary by itself. It's a long time process because its includes lots of terms such as content preprocessing, word embedding, fundamental model design, discourse rules, validation of test and training data, attention mechanism, supervised, reinforcement learning and so forth. We are going to give a review on some papers about ATS and will discuss about the pros and cons of their models.
