Informative and Controllable Opinion Summarization
Table 1: Automatic evaluation results. Systems whose results are taken from wang2016neural wang2016neural are marked with an asterisk *. Best performing results per metric are boldfaced.
['Model', 'METEOR', 'ROUGE-SU4', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
The first block shows one-pass systems, both supervised ( SubModular, SummaRunner) and unsupervised (LexRank, Opinosis). We can see that SummaRunner is the best performing system in this block; despite being extractive, it benefits from training data and the ability of neural models to learn task-specific representations. Our implementation of an EA-based system, SummaRunner+S2S+Copy, improves over the purely extractive SummaRunner and the previously reported best EA-based system, Regress+S2S. The third block presents two models using the proposed CA framework. Both systems outperform all other models across all metrics; AE+Att+Copy+Salient is the best model overall which exploits information about all documents and most salient ones.