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Despite recent progress in abstractive summarization, systems still sufferfrom faithfulness errors. While prior work has proposed models that improvefaithfulness, it is unclear whether the improvement comes from an increasedlevel of extractiveness of the model outputs as one naive way to improvefaithfulness is to make summarization models more extractive. In this work, wepresent a framework for evaluating the effective faithfulness of summarizationsystems, by generating a faithfulnessabstractiveness trade-off curve thatserves as a control at different operating points on the abstractivenessspectrum. We then show that the Maximum Likelihood Estimation (MLE) baseline aswell as a recently proposed method for improving faithfulness, are both worsethan the control at the same level of abstractiveness. Finally, we learn aselector to identify the most faithful and abstractive summary for a givendocument, and show that this system can attain higher faithfulness scores inhuman evaluations while being more abstractive than the baseline system on twodatasets. Moreover, we show that our system is able to achieve a betterfaithfulness-abstractiveness trade-off than the control at the same level ofabstractiveness.

Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization