Bottom-Up Abstractive Summarization
Table 6: Results on CNNDM when adding one inference penalty at a time.
['Data', 'R-1', 'R-2', 'R-L']
We next analyze the effect of the inference-time loss functions. We observe that all three penalties improve all three scores, even when added on top of the other two. This further indicates that the unmodified Pointer-Generator model has already learned an appropriate representation of the abstractive summarization problem, but is limited by its ineffective content selection and inference methods.