Query-Focused Opinion Summarization for User-Generated Content
Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers.
['[BOLD] Yahoo! Answer', '[BOLD] Yahoo! Answer Dispersion [ITALIC] sum', '[BOLD] Yahoo! Answer Dispersion [ITALIC] sum', '[BOLD] Yahoo! Answer Dispersion [ITALIC] min', '[BOLD] Yahoo! Answer Dispersion [ITALIC] min']
Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. Topical vs. Lexical). Results on Yahoo! Meanwhile, Cosine using TFIDF is better at measuring content coverage than WordNet-based semantic measurement, and this may due to the limited coverage of WordNet on verbs. This is also true for dissimilarity metrics. This indicates that optimal dispersion function varies by genre. Topical-based dissimilarity also marginally outperforms the other two metrics in blog data.