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Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness.
Bertscore: Evaluating text generation with bert
Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2019 · 1904
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On Faithfulness and Factuality in Abstractive Summarization
Maynez, J.; Narayan, S.; Bohnet, B.; and McDonald, R. 2020 · 1919
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MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wang, W.; Wei, F.; Dong, L.; Bao, H.; Yang, N.; and Zhou, M. 2020 · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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Extractive summarization as text matching
Zhong, M.; Liu, P.; Chen, Y.; Wang, D.; Qiu, X.; and Huang, X. 2020 · 2004
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Constrained abstractive summarization: Preserving factual consistency with constrained generation
Mao, Y.; Ren, X.; Ji, H.; and Han, J. 2020 · 2010
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PySBD: Pragmatic sentence boundary disambiguation
Sadvilkar, N.; and Neumann, M. 2020 · 2010
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Automatic summarization
Nenkova, A.; McKeown, K.; et al. 2011 · 2011
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MIMIC-III, a freely accessible critical care database
Johnson, A. E.; Pollard, T. J.; Shen, L.; Lehman, L.-w. H.; Feng, M.; Ghassemi, M.; Moody, B.; Szolovits, P.; Anthony Celi, L.; and Mark, R. G. 2016 · 2016
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Simple unsupervised keyphrase extraction using sentence embeddings
Bennani-Smires, K.; Musat, C.; Hossmann, A.; Baeriswyl, M.; and Jaggi, M. 2018 · 2018
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On the summarization of consumer health questions
Abacha, A. B.; and Demner-Fushman, D. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
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KeyBERT: Minimal keyword extraction with BERT
Grootendorst, M. 2020 · 2020
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Zhang, J.; Zhao, Y.; Saleh, M.; and Liu, P. 2020 · 2020
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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Gu, Y.; Tinn, R.; Cheng, H.; Lucas, M.; Usuyama, N.; Liu, X.; Naumann, T.; Gao, J.; and Poon, H. 2021 · 2021
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DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
He, P.; Liu, X.; Gao, J.; and Chen, W. 2021 · 2021
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QuestEval: Summarization asks for fact-based evaluation
Scialom, T.; Dray, P.-A.; Gallinari, P.; Lamprier, S.; Piwowarski, B.; Staiano, J.; and Wang, A. 2021 · 2021
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SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
Laban, P.; Schnabel, T.; Bennett, P. N.; and Hearst, M. A. 2022 · 2022
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Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
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From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting. arXiv
Addams, G.; Fabbri, A.; Ladhak, F.; Lehman, E.; and Elhadad, N. 2023 · 2023
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Aci-bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation
Yim, W.-w.; Fu, Y.; Ben Abacha, A.; Snider, N.; Lin, T.; and Yetisgen, M. 2023 · 2023
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Booookscore: A systematic exploration of book-length summarization in the era of llms
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Factually consistent summarization via reinforcement learning with textual entailment feedback
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