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- W4313444301 abstract "Text summarization is one of the most efficient and essential technique in this digital world in which we take out the important and meaningful information of a big text. Such techniques save us a lot of our meaningful time and can also be used in many other applications. Be it audio or video, once if we get the captions of them, we can summarize the text of captions and thereby we can give the summary of the audio or video, this could be one of the extensive usages of text summarization. In this paper, we explore various types of text summarization, majorly extractive summarizer and abstractive summarizer, which are discussed in detail. Especially, we focus more on abstractive summarizer and implement them using two different datasets with two different deep learning models. This paper may be assumed to be more biased on sequence models (attention model), an interesting deep learning model which is widely used in solving sequence-to-sequence problems." @default.
- W4313444301 created "2023-01-06" @default.
- W4313444301 creator A5017958384 @default.
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- W4313444301 date "2023-01-01" @default.
- W4313444301 modified "2023-09-23" @default.
- W4313444301 title "Comparative Study of Abstractive Summarizers (Sequence2Sequence Models)" @default.
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- W4313444301 doi "https://doi.org/10.1007/978-981-19-5868-7_48" @default.
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