@article{Liu_Lu_Yang_Qu_Zhu_Li_2018, title={Generative Adversarial Network for Abstractive Text Summarization}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/12141}, DOI={10.1609/aaai.v32i1.12141}, abstractNote={ <p> In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Linqing and Lu, Yao and Yang, Min and Qu, Qiang and Zhu, Jia and Li, Hongyan}, year={2018}, month={Apr.} }