Chinese Automatic Summarization Based on Thematic Sentence Discovery
In this paper, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to obtain summary based on similarity of thematic sentences, which use terms as features rather than words, and employs term length term frequency (TLTF) to compute weight of terms to obtain features. Furthermore, it uses an improved k-means method to cluster sentences, and compute similarity of thematic sentences according to clustering results. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.