1. Colo: A contrastive learning based re-ranking framework for one-stage summarization;Chenxin An;Proceedings of the 29th International Conference on Computational Linguistics,2022
2. Correcting diverse factual errors in abstractive summarization via post-editing and language model infilling;Hannaneh Vidhisha Balachandran;Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing,2022
3. One spring to rule them both: Symmetric amr semantic parsing and generation without a complex pipeline;Michele Bevilacqua;Proceedings of the AAAI Conference on Artificial Intelligence,2021
4. Factual error correction for abstractive summarization models;Meng Cao;Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP),2020
5. Cliff: Contrastive learning for improving faithfulness and factuality in abstractive summarization;Shuyang Cao;Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing,2021