Generic text summarization using local and global properties of sentences

With the proliferation of text data on the World-Wide Web, the development of methods for automatically summarizing these data becomes more important. Here, 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 exploit both the local and global properties of sentences. The local property can be considered as clusters of significant words within each sentence, while the global property can be though of as relations of all sentences in the document. These two properties are combined to get a single measure reflecting the informativeness of sentences. Experimental results show that our approach compares favorably to a commercial text summarizer.