Unsupervised Opinion Summarization with Noising and Denoising
Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix.
['Model', 'RT', 'Yelp']
Our experiments confirm that increasing the size of the synthetic data improves performance, and that both segment and document noising are useful. We also show that explicit denoising, partial copy, and the discriminator help achieve best results. Finally, human-labeled categories (instead of LDA topics) decrease model performance, which suggests that more useful labels can be approximated by automatic means.