Extractive Text Summarization Using Word Vector Embedding

These days, text summarization is an active research field to identify the relevant information from large documents produced in various domains such as finance, news media, academics, politics, etc. Text summarization is the process of shortening the documents by preserving the important contents of the text. This can be achieved through extractive and abstractive summarization. In this paper, we have proposed an approach to extract a good set of features followed by neural network for supervised extractive summarization. Our experimental results on Document Understanding Conferences 2002 dataset show the effectiveness of the proposed method against various online extractive text summarizers.