| Evaluating the factual consistency of abstractive text summarization W Kryściński, B McCann, C Xiong, R Socher arXiv preprint arXiv:1910.12840, 2019 | 466 | 2019 |
| Summeval: Re-evaluating summarization evaluation AR Fabbri, W Kryściński, B McCann, C Xiong, R Socher, D Radev Transactions of the Association for Computational Linguistics 9, 391-409, 2021 | 359 | 2021 |
| Neural text summarization: A critical evaluation W Kryściński, NS Keskar, B McCann, C Xiong, R Socher arXiv preprint arXiv:1908.08960, 2019 | 327 | 2019 |
| Improving abstraction in text summarization W Kryściński, R Paulus, C Xiong, R Socher arXiv preprint arXiv:1808.07913, 2018 | 162 | 2018 |
| Ctrlsum: Towards generic controllable text summarization J He, W Kryściński, B McCann, N Rajani, C Xiong arXiv preprint arXiv:2012.04281, 2020 | 86 | 2020 |
| Abstraction of text summarization R Paulus, W Kryscinski, C Xiong US Patent 10,909,157, 2021 | 53 | 2021 |
| Booksum: A collection of datasets for long-form narrative summarization W Kryściński, N Rajani, D Agarwal, C Xiong, D Radev arXiv preprint arXiv:2105.08209, 2021 | 47 | 2021 |
| Exploring neural models for query-focused summarization J Vig, AR Fabbri, W Kryściński, CS Wu, W Liu arXiv preprint arXiv:2112.07637, 2021 | 22 | 2021 |
| Folio: Natural language reasoning with first-order logic S Han, H Schoelkopf, Y Zhao, Z Qi, M Riddell, L Benson, L Sun, E Zubova, ... arXiv preprint arXiv:2209.00840, 2022 | 21* | 2022 |
| Long document summarization with top-down and bottom-up inference B Pang, E Nijkamp, W Kryściński, S Savarese, Y Zhou, C Xiong arXiv preprint arXiv:2203.07586, 2022 | 19 | 2022 |
| Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors L Tang, T Goyal, AR Fabbri, P Laban, J Xu, S Yavuz, W Kryściński, ... arXiv preprint arXiv:2205.12854, 2022 | 17 | 2022 |
| FeTaQA: Free-form table question answering L Nan, C Hsieh, Z Mao, XV Lin, N Verma, R Zhang, W Kryściński, ... Transactions of the Association for Computational Linguistics 10, 35-49, 2022 | 13* | 2022 |
| Summvis: Interactive visual analysis of models, data, and evaluation for text summarization J Vig, W Kryściński, K Goel, NF Rajani arXiv preprint arXiv:2104.07605, 2021 | 13 | 2021 |
| HydraSum: Disentangling Style Features in Text Summarization with Multi-Decoder Models T Goyal, N Rajani, W Liu, W Kryściński Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 12* | 2022 |
| Improving the faithfulness of abstractive summarization via entity coverage control H Zhang, S Yavuz, W Kryscinski, K Hashimoto, Y Zhou arXiv preprint arXiv:2207.02263, 2022 | 11 | 2022 |
| Sketch-Fill-AR: A persona-grounded chit-chat generation framework M Shum, S Zheng, W Kryściński, C Xiong, R Socher arXiv preprint arXiv:1910.13008, 2019 | 7 | 2019 |
| Long sequence modeling with xgen: A 7b llm trained on 8k input sequence length E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ... Salesforce AI Research Blog, 2023 | 6 | 2023 |
| What's New? Summarizing Contributions in Scientific Literature H Hayashi, W Kryściński, B McCann, N Rajani, C Xiong arXiv preprint arXiv:2011.03161, 2020 | 6 | 2020 |
| Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization A Pagnoni, AR Fabbri, W Kryściński, CS Wu arXiv preprint arXiv:2212.10449, 2022 | 4 | 2022 |
| LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond P Laban, W Kryściński, D Agarwal, AR Fabbri, C Xiong, S Joty, CS Wu arXiv preprint arXiv:2305.14540, 2023 | 3 | 2023 |
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