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 4: Evaluation of linguistic quality.
['System', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'AVG']
On average, the two systems perform very closely. System 22 is an extraction-based method that picks the original sentences, hence it achieves higher score in Q1 grammaticality, while our approach has some new sentences with grammar mistakes, which is a common problem for abstractive methods and deserves more future research effort. For Q4 focus, our score is higher than System 22, which reveals that our summary sentences are relatively more cohesive. The score of Q3 referential clarity shows that the referential relation is basically clear in our summaries, even when new sentences are automatically generated. In general, ignoring the grammaticality scores, our system still performs better than System 22. Specifically, the average scores of our system and System 22 on the last four questions are 3.37 and 3.33 respectively.