Collaborative Summarization: When Collaborative Filtering Meets Document Summarization

We propose a new way of generating personalized single document summary by combining two complementary methods: collaborative filtering for tag recommendation and graph-based affinity propagation. The proposed method, named by Collaborative Summarization, consists of two steps iteratively repeated until convergence. In the first step, the possible tags of one user on a new document are predicted using collaborative filtering which bases on tagging histories of all users. The predicted tags of the new document are supposed to represent both the key idea of the document itself and the special content of interest to that specific user. In the second step, the predicted tags are used to guide graph-based affinity propagation algorithm to generate personalized summarization. The generated summary is in turn used to fine tune the prediction of tags in the first step. The most intriguing advantage of collaborative summarization is that it harvests human intelligence which is in the form of existing tag annotations of webpages, such as delicious.com bookmark tags, to tackle a complex NLP task which is very difficult for artificial intelligence alone. Experiment on summarization of wikipedia documents based on delicious.com bookmark tags shows the potential of this method.