Text summarization as an assistive technology

Automated text summarization can be applied as an assistive tool for people with vision deficiency as well as with language understanding or attention deficit disorders. In this paper, we introduce an unsupervised graph based ranking model for text summarization. Our model builds a graph by collecting words, and their lexical relationships from the document. We apply a handful of available semantic information (definition, sentimental polarity) of words to enhance edge-weights (interconnectivity) between nodes (words). After applying a polarity based ranking algorithm over the graph we collect a subset of high-ranked and low-ranked words, name those as keywords. We, then, extract sentences that possess a higher rank defined by the rank vector of keywords. Sentences extracted in this manner correlate with each other and express the summary of the document quite successfully. Summaries formed by our model can appease readers with vision difficulties while keep them updated.