Opinion Summarization For Short Texts Based On Bm25 And Syntactic Parsing

Online short texts of hot topics submitted to social media by users can provide valuable personal opinions, which are useful for service providers and individuals. However, it is difficult for readers to grasp the main opinions of massive short texts. In this paper, to cope with the summarization challenge of short texts, we proposed a novel approach, which makes full use of BM25 to weight each short text and syntactic parsing to generate important information of each opinion cluster. The approach also utilizes the feature pruning to reduce the dimensions of the vectors. We conduct our experiments on real datasets and evaluate the results by standard metrics and manual evaluation. The experimental results show that our proposed approach improves the accuracy when compared to the state-of-the-art method.