  Since its introduction, the transformers architecture has seen great adoption
in NLP applications, but it also has limitations. Although the self-attention
mechanism allows for generating very rich representations of the input text,
its effectiveness may be limited in specialized domains such as legal, where,
for example, language models often have to process very long texts. In this
paper, we explore alternatives to replace the attention-based layers with
simpler token-mixing mechanisms: Hartley and Fourier transforms. Using these
non-parametric techniques, we train models with long input documents from
scratch in the legal domain setting. We also introduce a new hybrid Seq2Seq
architecture, a no-attention-based encoder connected with an attention-based
decoder, which performs quite well on existing summarization tasks with much
less compute and memory requirements. We believe that similar, if not better
performance, as in the case of long correlations of abstractive text
summarization tasks, can be achieved by adopting these simpler infrastructures.
This not only makes training models from scratch accessible to more people, but
also contributes to the reduction of the carbon footprint during training.
