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Deep recurrent neural networks for abstractive text summarization

Klönne, Marie (2018) Deep recurrent neural networks for abstractive text summarization. Bachelor's, Universität Bielefeld.

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Abstract

This thesis is dealing with the creation of a model for abstractive text summarization. For this purpose, recurrent neural networks are used to generate accurate summaries of given texts in the correct English language and context. We are appending a combination of recurrent neural network with hierarchical attention followed by Long Short Term Memory Networks (LSTM) building an auto-encoder structure. This work shows a possible upgradeable variant for automatically summarizing texts and can now be expanded for further research. The abstract compilation of texts is still in its infancy, and there are still many different open possibilities waiting to be realized.

Item URL in elib:https://elib.dlr.de/119932/
Document Type:Thesis (Bachelor's)
Title:Deep recurrent neural networks for abstractive text summarization
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Klönne, Mariemarie (at) kloenne.comUNSPECIFIED
Date:8 May 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:35
Status:Published
Keywords:machine learning, deep learning, test mining, lstm, recurrent neural networks,
Institution:Universität Bielefeld
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben SISTEC
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > Distributed Systems and Component Software
Institut of Simulation and Software Technology
Deposited By: Schreiber, Andreas
Deposited On:20 Jul 2018 09:42
Last Modified:31 Jul 2019 20:17

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