Reader-Aware Multi-Document Summarization via Sparse CodingThe 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).
Table 1: Results on our data set.
['[BOLD] System', '[BOLD] Rouge-1', '[BOLD] Rouge-2', '[BOLD] Rouge-SU4']
The reasons are as follows: (1) Our sparse coding model directly assigns coefficient values as expressiveness scores to the news sentences, which are obtained by minimizing the global semantic space reconstruction error and are able to precisely represent the importance of sentences. (2) The model can jointly consider news content and reader comments taking into account of more reader-aware information. (3) In our sparse coding model, we weight the reconstruction error by a prior knowledge, i.e., paragraph position, which can improve the summarization performance significantly. (4) Our unified optimization framework can further filter the unimportant NPs and VPs and generate the compressed summaries. (5) We conduct entity rewriting in the unified optimization framework in order to improve the linguistic quality.