title: Using Transformer for Text-Summarization
body: <p>I am using huggingface transformer models for <strong>text-summarization</strong>.
  Currently I am testing different models such as <strong>T5</strong> and <strong>Pegasus</strong>.
  Now these models were trained for summarizing Big Texts into very short like a maximum of two sentences. Now I have the task, that I want summarizations, that are about half the size of the text, ergo the generated summaries are too small for my purpose.</p>
  <p>My question now is, if there is a way to tell the model that another sentence came before?
  Kind of similar to the logic inside stateful RNNs (although I know they work completly different).
  If yes, I could summarize small windows over the sentences always with the information which content came before.</p>
  <p>Is that just a thing of my mind? I cant believe that I am the only one, who wants to create shorter summaries, but not only 1 or two sentence long ones.</p>
  <p>Thank you</p>