Table-to-Text　generation　aims　to　generate　descriptions　which　can　be　viewed　as　a　set　of　field-value　records　for　factual　tables.　Despite　the　significant　progress,　the　state-of-the-art　models　suffer　from　two　major　issues:　Nonfluency　and　Divergence.　Nonfluency　means　descriptions　generated　by　models　are　not　as　fluent　as　those　generated　by　humans,　and　thus　can　be　distinguished　easily.　Divergence　refers　to　the　fact　that　the　generated　sentences　contain　information　which　can　not　be　concluded　from　factual　tables.　This　could　be　attributed　to　that　most　neural　models　are　trained　with　the　Maximum　Likelihood　Estimation　(MLE)　loss　and　use　divergence-contained　references　as　the　ground　truth,　which　forces　the　models　to　learn　what　cannot　be　inferred　from　the　source　to　some　extent.　Motivated　by　the　limitations　of　current　models,　we　propose　a　novel　GAN-based　model　with　adversarial　learning　mechanism,　which　simultaneously　trains　a　generative　model　G　and　a　discriminative　model　D,　to　address　Nonfluency　and　Divergence　issues　in　Table-to-Text　generation.　Specifically,　we　build　the　generator　G　as　an　agent　of　reinforcement　learning　with　a　sequence-to-sequence　architecture,　which　takes　the　raw　data　as　input　and　predicts　the　generated　sentences.　Meanwhile,　we　build　the　discriminator　D　with　a　Convolutional　Neural　Network　(CNN)　to　calculate　rewards　to　measure　the　fluency　of　generations.　To　judge　the　fidelity　of　generations　with　regard　to　the　original　table　more　accurately,　we　also　calculate　the　rewards　from　BLEU　Table.　With　the　fusion　rewards　from　CNN　and　BLEU-Table,　our　methods　outperform　the　baselines　by　a　large　margin　on　the　WikiBio　and　Wiki3C　benchmarks　evaluated　with　BLEU,　ROURGE,　and　PARENT.　Specifically,　our　models　achieve　49.0　(BLEU-4),　37.8　(ROUGE-4)　and　45.4　(PARENT)　on　WikiBio,　as　well　as　12.9　(BLEU-4)　and　6.9　(ROUGE-4)　on　Wiki3C.　More　importantly,　we　construct　a　new　Wiki3C　dataset　that　improves　the　insufficiency　of　datasets　and　promote　the　progress　in　Table-to-Text　generation.　(c)　2021　Elsevier　B.V.　All　rights　reserved.