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Abstractive Text Summarization Using Artificial Intelligence

Authors: Chandu Parmar; Ranjan Chaubey; Kirtan Bhatt;

Abstractive Text Summarization Using Artificial Intelligence

Abstract

Text summarization is the process of creating concise summary of text. There are two main approaches to summarization namely extractive and abstractive method. Most of the system summaries use extractive method. Amongst few abstractive models available there are two models namely sequence to sequence and LSTM bidirectional model. In this work,we compare the performance of above two models using ROUGE and BLEU score on Amazon reviews and CNN news dataset.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Top 10%
Top 10%
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