Abstractive Document Summarisation using Generative Adversarial Networks

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/255471
Download file(s):
File Description SizeFormat 
255471.pdfFulltext1.89 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Abstractive Document Summarisation using Generative Adversarial Networks
Authors: Svensson, Karl
Abstract: The use of automatically generated summaries for long texts is commonly used in digital services. In this thesis, one method for such document summarisation is created by combining existing techniques for abstractive document summarization with LeakGAN – a successful approach at text generation using generative adversarial networks (GAN). The resulting model is tested on two different datasets originating from conventional newspapers and the world’s largest online community: Reddit. The datasets are examined and several important differences are highlighted. The evaluations show that the summaries generated by the model do not correlate with the corresponding documents. Possible reasons are discussed and several suggestions for future research are presented.
Keywords: Matematik;Mathematics
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
Chalmers University of Technology / Department of Mathematical Sciences
URI: https://hdl.handle.net/20.500.12380/255471
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.