User-oriented document summarization through vision-based eye-tracking

We propose a new document summarization algorithm which is personalized. The key idea is to rely on the attention (reading) time of individual users spent on single words in a document as the essential clue. The prediction of user attention over every word in a document is based on the user's attention during his previous reads, which is acquired via a vision-based commodity eye-tracking mechanism. Once the user's attentions over a small collection of words are known, our algorithm can predict the user's attention over every word in the document through word semantics analysis. Our algorithm then summarizes the document according to user attention on every individual word in the document. With our algorithm, we have developed a document summarization prototype system. Experiment results produced by our algorithm are compared with the ones manually summarized by users as well as by commercial summarization software, which clearly demonstrates the advantages of our new algorithm for user-oriented document summarization.