Affiliations: K.U. Leuven, Leuven-Heverlee, Belgium | [x] IRPI-CNR, Torino, Italy | [y] University of Torino, Torino, Italy | [z] University of Paris-Nord, Paris, France
Abstract: With the growing number of document sets accessible online, tracking their evolution over time (story tracking) became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate how graph structure can be used for detecting and discovering new developments in the story. First we investigate the possibly interesting graph properties for development detection. We continue by investigating how graph structure can be linked to the sentences representing developments. For this we create an evaluation framework which bridges the gap between temporal text mining patterns and sentences. We apply this framework to evaluate our method against other temporal text mining methods. Our experiments show that story graphs perform at similar levels overall, but provide distinctive advantages in some settings.
Keywords: Temporal text mining, graphical text summarization, evaluation of temporal text mining, dynamic graphs, burstiness, enews mining
