Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings
Table 1: ROUGE-1 (R-1), ROUGE-2 (R-2) and ROUGE-SU4 (R-SU4) scores for our proposed token-level summarization approaches along with two baselines, supervised methods and the Upperbound (only using DRDAs). — all use True 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']
How do our approach perform when compared with supervised learning approaches? For a better comparison, we also provide summarization results by using supervised systems along with an upperbound. A three-fold cross validation is conducted for both methods. and STM are shown to outperform the baselines and even the CRF. Meanwhile, by adding context information, both LocalLDA and STM can get better ROUGE-1 recall than the supervised methods, even higher than the provided upperbound which is computed by only using DRDAs. This shows the DomSum framework can leverage context to compensate the summaries.