Conceptual Text Summarizer: A new model in continuous vector space

Traditional methods of summarization are not cost-effective and possible today. Extractive summarization is a process that helps to extract the most important sentences from a text automatically and generates a short informative summary. In this work, we propose an unsupervised method to summarize Persian texts. This method is a novel hybrid approach that clusters the concepts of the text using deep learning and statistical methods. Although the proposed method is language independent, we focus on Persian text summarization in this work. First we produce a word embedding based on Hamshahri2 corpus and a dictionary of word frequencies. Then the proposed algorithm extracts the keywords of the document, clusters its concepts, and finally ranks the sentences to produce the summary. We evaluated the proposed method on Pasokh single document dataset using the ROUGE evaluation measure. Without using any hand-crafted features, our proposed method achieves competitive performance. ROUGE-3 recall score for system summaries generated with 25% compression ratio on Pasokh corpus is 0.27.