Automatic summary generation from single document using information gain

Abstract

With tons of information pouring in every day over Internet, it is not easy to read each and every document. The information retrieval from search engine is still far greater than that a user can handle and manage. So there is need of presenting the information in a summarized way. In this paper, an automatic abstractive summarization technique from single document is proposed. The sentences in the text are identified first. Then from those sentences segments, unique terms are identified. A Term-Sentence matrix is generated, where the column represents the sentences and the row represents the terms. The entries in the matrix are weight from information gain. Column with a maximum cosine similarity is selected as first sentence of the summary sentence and likewise. Results over documents indicate that the performance of the proposed approach compares very favorably with other approaches. © 2010 Springer-Verlag Berlin Heidelberg.