Transductive Learning over Automatically Detected Themes for Multi-Document Summarization
Massih Amini and Nicolas Usunier
In: SIGIR 2011, 24-28 July 2011, Beijing, China.
We propose a new mthod for query-biased multi-document summarization, based on sentence extraction. The summary of multiple documents is created in two steps. Sentences are first clustered; where each cluster corresponds to one of the main themes present in the collection. Inside each theme, sentences are then ranked using a transductive learning to rank algorithm based on RankNet, in order to better identify those which are relevant to the query. The final summary contains the top-ranked sentences of each theme. Our approach is validated on DUC 2006 and DUC 2007 datasets.