Focused Meeting Summarization via Unsupervised Relation Extraction
Table 4: ROUGE-1 (R-1), ROUGE-2 (R-2) and ROUGE-SU4 (R-SU4) scores for summaries produced by the baselines, GRE [Hachey2009]’s best results, the supervised methods, our method and an upperbound — all with perfect/true DRDA clusterings.
['[EMPTY]', '[BOLD] True Clusterings  [BOLD] R-1', '[BOLD] True Clusterings  [BOLD] R-1', '[BOLD] True Clusterings  [BOLD] R-1', '[BOLD] True Clusterings  [BOLD] R-2', '[BOLD] True Clusterings  [BOLD] R-SU4']
Note that for GRE based approach, we only list out their best results for utterance-level summarization. If using the salient relation instances identified by GRE as the summaries, the ROUGE results will be significantly lower. When measured by ROUGE-2, our method still have better or comparable performances than other unsupervised methods. Moreover, our system achieves F scores in between those of the supervised learning methods, performing better than the CRF in both recall and F score. The recall score for the upperbound in ROUGE-1, on the other hand, indicates that there is still a wide gap between the extractive summaries and human-written abstracts: without additional lexical information (e.g., semantic class information, ontologies) or a real language generation component, recall appears to be a bottleneck for extractive summarization methods that select content only from decision-related dialogue acts (DRDAs).