Summarize What You Are Interested In: An Optimization Framework for Interactive Personalized Summarization

Most traditional summarization methods treat their outputs as static and plain texts, which fail to capture user interests during summarization because the generated summaries are the same for different users. However, users have individual preferences on a particular source document collection and obviously a universal summary for all users might not always be satisfactory. Hence we investigate an important and challenging problem in summary generation, i.e., Interactive Personalized Summarization (IPS), which generates summaries in an interactive and personalized manner. Given the source documents, IPS captures user interests by enabling interactive clicks and incorporates personalization by modeling captured reader preference. We develop experimental systems to compare 5 rival algorithms on 4 instinctively different datasets which amount to 5197 documents. Evaluation results in ROUGE metrics indicate the comparable performance between IPS and the best competing system but IPS produces summaries with much more user satisfaction according to evaluator ratings. Besides, low ROUGE consistency among these user preferred summaries indicates the existence of personalization.