Abstractive Multi-Document Summarization via Phrase Selection and Merging The work described in this paper is substantially supported by grants from the Research and Development Grant of Huawei Technologies Co. Ltd (YB2013090068/TH138232) and the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Codes: 413510 and 14203414). The work was done when Weiwei Guo was in Columbia University
Table 3: Performance under ROUGE metric.
['System', '[BOLD] ROUGE-2 P', '[BOLD] ROUGE-2 R', '[BOLD] ROUGE-2 F1', '[BOLD] ROUGE-SU4 P', '[BOLD] ROUGE-SU4 R', '[BOLD] ROUGE-SU4 F1']
As mentioned above, we favor the pyramid evaluation over the ROUGE score because it can measure the summary quality beyond simply string matching. Here, we also provide ROUGE score for our reference. Our performance is slightly better than System 22, and it is not as good as System 43 and 17. The reason is that System 43 and 17 used category-specific features and trained the feature weights with the category information in TAC 2010 data. These features help them select better category-specific content for the summary. However, the usability of such features depends on the availability of predefined categories in the summarization task, as well as the availability of training data with the same predefined categories for estimating feature weights. Therefore, the adaptability of these methods is limited to some extent. In contrast, our framework does not define any category-specific feature and only uses TAC 2010 data to tune the parameters for general summarization purpose.