A Supervised Approach For Extractive Text Summarization Using Minimal Robust Features

Over the past decade or so the amount of data on the Internet has increased exponentially. Thus arises the need for a system that processes this immense amount of data into useful information that can be easily understood by the human brain. Text summarization is one such popular method under research that opens the door to dealing with voluminous data. It works by generating a compressed version of the given document by preserving the important details. Text summarization can be classified into Abstractive and Extractive summarization. Extractive summarization methods reduce the burden of summarization by selecting a subset of sentences that are important from the actual document. Although there are many suggested approaches to this methodology, researchers in the field of natural language processing are especially attracted to extractive summarization. The model that is introduced in this paper implements extractive text summarization using a supervised approach with the minimalistic features extracted. The results reported by the proposed model are quite satisfactory with an average ROUGE-1 score of 0.51 across 5 reported domains on the BBC news article dataset.