Timestamped graphs: Evolutionary models of text for multi-document summarization

Abstract

Current graph-based approaches to automatic text summarization, such as LexRank and TextRank, assume a static graph which does not model how the input texts emerge. A suitable evolutionary text graph model may impart a better understanding of the texts and improve the summarization process. We propose a timestamped graph (TSG) model that is motivated by human writing and reading processes, and show how text units in this model emerge over time. In our model, the graphs used by LexRank and TextRank are specific instances of our timestamped graph with particular parameter settings. We apply timestamped graphs on the standard DUC multi-document text summarization task and achieve comparable results to the state of the art.