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In this paper, we design an auto encoder based off of Google's FNetArchitecture in order to generate text from a subset of news stories containedin Google's C4 dataset. We discuss previous attempts and methods to generatetext from autoencoders and non LLM Models. FNET poses multiple advantages toBERT based encoders in the realm of efficiency which train 80% faster on GPUsand 70% faster on TPUs. We then compare outputs of how this autencoder perfromson different epochs. Finally, we analyze what outputs the encoder produces withdifferent seed text.

An FNet based Auto Encoder for Long Sequence News Story Generation