Newspaper Article Summarization Using Combinational Method

Text summarization produces a concise amount of data out of document which may represent the core information contained in the document. In this paper, a combinational approach has been used to generate summary from newspaper article that uses a number of features to rank sentences of the article. Graph based approach with minimal text sentiment analysis and the presence of category wise key phrases have been used. Sentences with the sentiment polarity of the overall document sentiment polarity can convey important information. Combination of multiple features can lead to the identification process of the significant sentences with which good summary can be formed. After the performance analysis of the approach, it has been observed that the technique can generate good summaries that are close to human written ones.