Extractive Single Document Summarization via Multi-feature Combination and Sentence Compression

摘要

In this paper, we attempt to extract and generate the short summary for the news article with the length limit of 60 Chinese characters. Firstly, we preprocess the news article by segmenting sentences and words, and then extract four kinds of central words to form the keyword dictionary based on parsing tree. After that, the four kinds of features, i.e. the sentence weight, the sentence similarity, the sentence position and the length of sentence, will be employed to measure the significance of each sentence. Finally, we extract two sentences in the descending order of significance score and compress them to get the summary for each news article. This approach can analyze the grammatical elements from original sentences in order to generate compression rules and trim syntactic elements according to their parsing trees. The evaluation results show that our system is efficient in Chinese news summarization.