  A timeline provides one of the most effective ways to visualize the important
historical facts that occurred over a period of time, presenting the insights
that may not be so apparent from reading the equivalent information in textual
form. By leveraging generative adversarial learning for important sentence
classification and by assimilating knowledge based tags for improving the
performance of event coreference resolution we introduce a two staged system
for event timeline generation from multiple (historical) text documents. We
demonstrate our results on two manually annotated historical text documents.
Our results can be extremely helpful for historians, in advancing research in
history and in understanding the socio-political landscape of a country as
reflected in the writings of famous personas.
