Abstractive summarization, knowledge-driven summarization, long document summarization
Intelligence Advanced Research Projects Activity (IARPA). Duration: September 2017 - October 2021.
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization. Shuyang Cao and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. [code]
Efficient Attentions for Long Document Summarization. Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, and Lu Wang. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021. [code and data]
Inference Time Style Control for Summarization. Shuyang Cao and Lu Wang. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), short paper, 2021. [code and data]
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization. Shuyang Cao and Lu Wang. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), short paper, 2021. [code]
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward. Luyang Huang, Lingfei Wu, and Lu Wang. Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2020. [code]
An Entity-Driven Framework for Abstractive Summarization. Eva Sharma, Luyang Huang, Zhe Hu, and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019. [code]
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards. Hou Pong Chan, Wang Chen, Lu Wang, and Irwin King. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019. [code]
Semi-Supervised Learning for Neural Keyphrase Generation. Hai Ye and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information. Lisa Fan, Dong Yu, and Lu Wang. NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and Language (IRASL), 2018.
This project is supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract # FA8650-17-C9116. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.