Summarizing with Encyclopedic Knowledge

This paper presents a topic-driven multidocument summarization approach that relies on linking documents to Wikipedia. Wikipedia provides structural support to retrieve relevant concepts from the documents to be summarized, and quantify the strength of the relations between them, thus expanding the topic. We identify concepts in the documents, and assign them scores that describe their relevance to the topic, their significance in general, and a machine-learned confidence that they should appear in the summary. Sentences are ranked according to the scores of the concepts within them and how much new information they provide. The best are extracted and compressed to form the summary. The system is trained and developed using the DUC 2005 and 2006 data. It was tested on the DUC 2007 data before deploying it on the update summarization task of TAC 2009. It performs 5th (compared to 30 peers) in DUC 2007, and 21st (of 52 peers) on the TAC 2009 update task.