Text document summarization using word embedding

摘要

Automatic text summarization essentially condenses a long document into a shorter format while preserving its information content and overall meaning. It is a potential solution to the information overload. Several automatic summarizers exist in the literature capable of producing high-quality summaries, but they do not focus on preserving the underlying meaning and semantics of the text. In this paper, we capture and preserve the semantics of text as the fundamental feature for summarizing a document. We propose an automatic summarizer using the distributional semantic model to capture semantics for producing high-quality summaries. We evaluated our summarizer using ROUGE on DUC-2007 dataset and compare our results with other four different state-of-the-art summarizers. Our system outperforms the other reference summarizers leading us to the conclusion that usage of semantic as a feature for text summarization provides improved results and helps to further reduce redundancies from the input source. (C) 2019 Published by Elsevier Ltd.