Learning-Based Single-Document Summarization withCompression and Anaphoricity Constraints
Table 2: Results for RST Discourse Treebank [Carlson et al.2001]. Differences between our system and the Tree Knapsack system of YoshidaEtAl2014 are not statistically significant, reflecting the high variance in this small (20 document) test set.
['[EMPTY]', 'ROUGE-1', 'ROUGE-2']
We also evaluate on the RST Discourse Treebank, of which 30 documents have abstractive summaries. Following \newciteHiraoEtAl2013, we use the gold EDU segmentation from the RST corpus but automatic RST trees. We break this into a 10-document development set and a 20-document test set. Our system is roughly comparable to Tree Knapsack here, and we note that none of the differences in the table are statistically significant. We also observed significant variation between multiple runs on this corpus, with scores changing by 1-2 ROUGE points for slightly different system variants.