The Extractive-Abstractive Axis: Measuring Content "Borrowing" in
  Generative Language Models
Nedelina Teneva
Generative language models produce highly abstractive outputs by design, in
contrast to extractive responses in search engines. Given this characteristic
of LLMs and the resulting implications for content Licensing & Attribution, we
propose the the so-called Extractive-Abstractive axis for benchmarking
generative models and highlight the need for developing corresponding metrics,
datasets and annotation guidelines. We limit our discussion to the text
modality.