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2020 | OriginalPaper | Buchkapitel

Abstractive Text Summarization Using Enhanced Attention Model

verfasst von : Rajendra Kumar Roul, Pratik Madhav Joshi, Jajati Keshari Sahoo

Erschienen in: Intelligent Human Computer Interaction

Verlag: Springer International Publishing

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Abstract

Text summarization is the technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information, and without losing the overall meaning. Although recent works are paying attentions in this domain, but still they have many limitations which need to be address.. This paper studies the attention model for abstractive text summarization and proposes three models named as Model 1, Model 2, and Model 3 which not only handle the limitations of the previous contemporary works but also strengthen the experimental results further. Empirical results on CNN/DailyMail dataset show that the proposed approach is promising.

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Vorheriges Kapitel Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect

Nächstes Kapitel Highlighted Word Encoding for Abstractive Text Summarization

Here \(\hat{w}_j^i\) isn’t an estimate of the word \(w_j^i\), but a prediction of the \(j^{th}\) word of the summary.

https://www.nltk.org/api/nltk.tokenize.html.

https://nlp.stanford.edu/projects/glove/.

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Titel: Abstractive Text Summarization Using Enhanced Attention Model
verfasst von: Rajendra Kumar Roul
Pratik Madhav Joshi
Jajati Keshari Sahoo
Verlag: Springer International Publishing
Buch: Intelligent Human Computer Interaction
Print ISBN: 978-3-030-44688-8

Electronic ISBN: 978-3-030-44689-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-44689-5_6

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