  Extractive compression is a challenging natural language processing problem.
This work contributes by formulating neural extractive compression as a parse
tree transduction problem, rather than a sequence transduction task. Motivated
by this, we introduce a deep neural model for learning
structure-to-substructure tree transductions by extending the standard Long
Short-Term Memory, considering the parent-child relationships in the structural
recursion. The proposed model can achieve state of the art performance on
sentence compression benchmarks, both in terms of accuracy and compression
rate.
