${\mathbf{h}}_e$ into two components, one which represents the structure in the document and the other represents the semantic information. This formulation was used by [@balachandran2020strucsum] for controlling structure in abstractive summarization. This work performs the split with respect to the dimensions of ${\mathbf{h}}_e$. The method forces the first $n$ dimensions of ${\mathbf{h}}_e$ to capture meaning and the latter to capture structure. @balachandran2020strucsum also show quantitative and qualitative analysis on the types of structures of documents learnt by this technique.

@romanov-etal-2019-adversarial decompose the encoder representation ${\mathbf{h}}_e$ into a form vector ${\mathbf{f}}$ and a meaning vector ${\mathbf{m}}$. During the training phase, a [*discriminator*]{} enforces ${\mathbf{m}}$ to not carry any information about the form using an adversarial loss and a [*motivator*]{} is used for a motivational loss that encourages ${\mathbf{f}}$ to carry the information about the form. The generation process can then be guided to adhere to the desired target form. As opposed to splitting ${\mathbf{h}}_e$ with respect to dimensions, this work learns subspaces ${\mathbf{W_m}}$ and ${\mathbf{W_f}}$ given by ${\mathbf{m}} = {\mathtt{tanh}}({\mathbf{W}}_m{\mathbf{h}}_e + {\mathbf{b}}_m)$ and ${\mathbf{f}} = {\mathtt{tanh}}({\mathbf{W}}_f{\mathbf{h}}_e + {\mathbf{b}}_f)$ respectively. When ${\mathbf{h}}_e$ is projected on ${\mathbf{W}}_m$, we get the meaning vector ${\mathbf{m}}$ and similarly when it is projected on ${\mathbf{W}}_f$ we get 