Articial intelligence is an area of technology which is rapidly growing. Considering it'sincreasing inuence in society, how available is it? This study attempts to create a web contentsummarizer using generative machine learning. Several concepts and technologies are explored, most notably sequence to sequence, transfer learning and recursive neural networks. The study later concludes how creating a purely generative summarizer is unfeasible on a hobbyist level due to hardware restrictions, showing that slightly more advanced machine learning techniques still are unavailable to non-specialized individuals. The reasons why are investigated in depth using an extensive theoretical section which initially explains how neural networks work, then natural language processing at large and finally how to create a generative recurrent articial neural network. Ethical and societal concerns concerning machine learning text generation is also discussed, along with alternative approaches to solving the task at hand.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-77167 |
| Date | January 2019 |
| Creators | Kinde, Lorentz |
| Publisher | Luleå tekniska universitet, Datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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