  Deep reinforcement learning (RL) has been a commonly-used strategy for the
abstractive summarization task to address both the exposure bias and
non-differentiable task issues. However, the conventional reward Rouge-L simply
looks for exact n-grams matches between candidates and annotated references,
which inevitably makes the generated sentences repetitive and incoherent. In
this paper, instead of Rouge-L, we explore the practicability of utilizing the
distributional semantics to measure the matching degrees. With distributional
semantics, sentence-level evaluation can be obtained, and semantically-correct
phrases can also be generated without being limited to the surface form of the
reference sentences. Human judgments on Gigaword and CNN/Daily Mail datasets
show that our proposed distributional semantics reward (DSR) has distinct
superiority in capturing the lexical and compositional diversity of natural
language.

Computation and Language;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing