Timestamped Graphs: Evolutionary Models of Text for Multi-document Summarization
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Current graph-based approaches to auto- matic text summarization, such as Le- xRank and TextRank, assume a static graph which does not model how the in- put texts emerge. A suitable evolutionary text graph model may impart a better un- derstanding 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 Tex- tRank are specific instances of our time- stamped graph with particular parameter settings. We apply timestamped graphs on the standard DUC multi-document text summarization task and achieve compara- ble results to the state of the art.