  The task of event extraction (EE) aims to find the events and event-related
argument information from the text and represent them in a structured format.
Most previous works try to solve the problem by separately identifying multiple
substructures and aggregating them to get the complete event structure. The
problem with the methods is that it fails to identify all the interdependencies
among the event participants (event-triggers, arguments, and roles). In this
paper, we represent each event record in a unique tuple format that contains
trigger phrase, trigger type, argument phrase, and corresponding role
information. Our proposed pointer network-based encoder-decoder model generates
an event tuple in each time step by exploiting the interactions among event
participants and presenting a truly end-to-end solution to the EE task. We
evaluate our model on the ACE2005 dataset, and experimental results demonstrate
the effectiveness of our model by achieving competitive performance compared to
the state-of-the-art methods.
