Abstractive Document Summarisation using Generative Adversarial Networks

dc.contributor.authorSvensson, Karl
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:48:33Z
dc.date.available2019-07-03T14:48:33Z
dc.date.issued2018
dc.description.abstractThe 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.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/255471
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectMatematik
dc.subjectMathematics
dc.titleAbstractive Document Summarisation using Generative Adversarial Networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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