text: 405,107,1169698492316405760,503452360,William Wang,"BLEU/ROUGE only captures exact lexical overlap. Can one optimize distributional semantic rewards using REINFORCE for abstractive summarization? In our #EMNLP2019 paper, we show that optimizing BERTscore is a viable new solution for deep RL in #NLProc. <LINK>",https://arxiv.org/abs/1909.00141,"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. ","Deep Reinforcement Learning with Distributional Semantic Rewards for