Abstractive Text Summarization By Using Deep Learning Models

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Soma Shrenikaa, GouriPriyaRamini, LunsavathBhagavathiDevi, B Geetavani

Abstract

Knowledge is perpetual, a person’s life time isn’t enough to absorb the whole knowledge of the universe. We all
are homo sapiens who believe in living beyond the nihilism. We try to seek more and more potentiality in our lives by
seeking knowledge. This knowledge these days is stored in various formats in huge repositories mostly in the form of
documents, sheets, photos, videos. One finds it difficult to comprehend this whole lot of information. There by, here comes
the need of text summarization. Summarization of documents, text, data is the vital part and a preliminary step in any field
whether it is business, social, art, or software. By using machine learning algorithms, the notion of text summarization can be
achieved with ease. In this paper we summarize the data as per our requirement i.e., it can be based on output. To achieve this
objective we are going to use abstractive summarization on single documents. In this process, we perform abstractive text
summarization by using Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformers.

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