Joint Modeling of Content and Discourse Relations in Dialogues
Table 4: ROUGE scores for phrase-based extractive summarization evaluated against human-constructed utterance-level extractive summaries and abstractive summaries. Our models that statistically significantly outperform SVM and liu2009unsupervised are highlighted with ∗ (p<0.05, paired t-test). Best ROUGE score for each column is in bold.
['[ITALIC] Extractive Summaries as Gold-Standard', '[ITALIC] Extractive Summaries as Gold-Standard', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-1', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-1', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-1', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-SU4', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-SU4', '[ITALIC] Extractive Summaries as Gold-Standard ROUGE-SU4']
Meanwhile, our system significantly outperforms the SVM-based classifiers when evaluated on ROUGE recall and F1, while achieving comparable precision. Compared to \newciteliu2009unsupervised, our system also yields better results on all metrics.