| Controlling length in abstractive summarization using a convolutional neural network Y Liu, Z Luo, K Zhu Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 96 | 2018 |
| Multi-turn response selection using dialogue dependency relations Q Jia, Y Liu, S Ren, KQ Zhu, H Tang arXiv preprint arXiv:2010.01502, 2020 | 41 | 2020 |
| Length control in abstractive summarization by pretraining information selection Y Liu, Q Jia, K Zhu Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 31 | 2022 |
| Keyword-aware abstractive summarization by extracting set-level intermediate summaries Y Liu, Q Jia, K Zhu Proceedings of the Web Conference 2021, 3042-3054, 2021 | 16 | 2021 |
| Zero-shot faithfulness evaluation for text summarization with foundation language model Q Jia, S Ren, Y Liu, KQ Zhu arXiv preprint arXiv:2310.11648, 2023 | 11 | 2023 |
| Post-training dialogue summarization using pseudo-paraphrasing Q Jia, Y Liu, H Tang, KQ Zhu arXiv preprint arXiv:2204.13498, 2022 | 11 | 2022 |
| Taxonomy of abstractive dialogue summarization: scenarios, approaches, and future directions Q Jia, Y Liu, S Ren, KQ Zhu ACM Computing Surveys 56 (3), 1-38, 2023 | 10 | 2023 |
| Reference-free summarization evaluation via semantic correlation and compression ratio Y Liu, Q Jia, K Zhu Proceedings of the 2022 conference of the North American Chapter of the …, 2022 | 9 | 2022 |
| Reducing repetition in convolutional abstractive summarization Y Liu, X Chen, X Luo, KQ Zhu Natural Language Engineering 29 (1), 81-109, 2023 | 6 | 2023 |
| In-sample curriculum learning by sequence completion for natural language generation Q Jia, Y Liu, H Tang, KQ Zhu arXiv preprint arXiv:2211.11297, 2022 | 4 | 2022 |
| Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation Y Liu, R Tao, S Guo, Y Yang arXiv preprint arXiv:2404.02616, 2024 | 1 | 2024 |
| Opinion Summarization by Weak-Supervision from Mix-structured Data Y Liu, Q Jia, K Zhu Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 1 | 2022 |
| A Convolutional Sequence-to-Sequence Attention Fusion Framework for Commonsense Causal Reasoning Z Luo, Y Liu, S Luo Mathematics 11 (23), 4796, 2023 | | 2023 |
Kenny Q. ZhuProfessor of Computer Science, University of Texas at ArlingtonVerified email at uta.edu