Word concept extraction using HOSVD for automatic text summarization

Computers understand little about the meaning of human language. Vector space models of semantics are beginning to overcome these limits. In this regard, one of the modern issues is using high dimensional data, which is formulated as tensors. Also, due to the increased information and texts, automatic text summarization has become one of the most important issues in information retrieval and natural language processing. In this paper, we propose a new method, using higher-order singular value decomposition (HOSVD) for extracting the concept of the words from word-document-time three-dimensional tensor and then select important sentences with more cosine similarity to this concept. In the following, we measure WordNet-based semantic similarity between sentences and remove redundancy sentences with less importance. The evaluation of the proposed method is done using the ROUGE evaluation on the DUC 2007 standard data set that the obtained results indicate the predominance of our method over many dominant systems.