Research on multi-document summarization merging the sentential semantic features

Multi-document summarization (MDS) is one of the key issues in the field of natural language processing. In order to extract compendious sentences to reflect more accurate theme of the multi-document, a new method was proposed to retrieve terse sentences. At first, some sentential semantic features (SSF), for example topic and predicate, were extracted based on a sentential semantic model (SSM). Then the sentence weight was calculated by building feature vector merging statistical features and SSF. Finally, sentences were extracted according to the feature weighting and maximal marginal relevance (MMR). A set of experiment show that the new method is effective, the average precision rate of summary can reach 66.7%, and the average recall rate can reach 65.5% when the compression ratio of summary is 15%. The results of experiments show that the SSF are effective on upgrading the affection of MDS.