Bottom-Up Abstractive Summarization
Table 2: Results on the NYT corpus, where we compare to RL trained models. * marks models and results by Paulus et al. (2017), and † results by Celikyilmaz et al. (2018).
['Method', 'R-1', 'R-2', 'R-L']
We see that the 2 point improvement compared to the baseline Pointer-Generator maximum-likelihood approach carries over to this dataset. Here, the model outperforms the RL based model by Paulus et al. The same can be observed when comparing ML and our Pointer-Generator. We suspect that a difference in summary lengths due to our inference parameter choices leads to this difference, but did not have access to their models or summaries to investigate this claim. This shows that a bottom-up approach achieves competitive results even to models that are trained on summary-specific objectives.