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Automatic evaluation of generated textual content presents an ongoing challenge within the field of NLP.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Statistics: Fourth International Student Edition
D. Freedman, R. Pisani, and R. Purves. 2007 · 2007
Earlier work this paper cites.
An investigation into the validity of some metrics for automatically evaluating natural language generation systems
Ehud Reiter and Anja Belz. 2009 · 2009
Earlier work this paper cites.
Spearman Rank Correlation: Overview . John Wiley & Sons, Ltd
Jerrold H. Zar. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
Better rewards yield better summaries: Learning to summarise without references
Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Studying summarization evaluation metrics in the appropriate scoring range
Maxime Peyrard. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
How to compare summarizers without target length? pitfalls, solutions and re-examination of the neural summarization literature
Simeng Sun, Ori Shapira, Ido Dagan, and Ani Nenkova. 2019 · 2019
Earlier work this paper cites.
Best practices for the human evaluation of automatically generated text
Chris van der Lee, Albert Gatt, Emiel van Miltenburg, Sander Wubben, and Emiel Krahmer. 2019 · 2019
Earlier work this paper cites.
MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019 · 2019
Earlier work this paper cites.
Re-evaluating evaluation in text summarization
Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, Pengfei Liu, and Graham Neubig. 2020 · 2020
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SUPERT: Towards new frontiers in unsupervised evaluation metrics for multi-document summarization
Yang Gao, Wei Zhao, and Steffen Eger. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Tangled up in BLEU: Reevaluating the evaluation of automatic machine translation evaluation metrics
Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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Smart: Sentences as basic units for text evaluation
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, and Shashi Narayan. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Later among the works it cites.
On the limitations of reference-free evaluations of generated text
Daniel Deutsch, Rotem Dror, and Dan Roth. 2022 · 2022
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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Unsupervised reference-free summary quality evaluation via contrastive learning
Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, and Shouling Ji. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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A training-free and reference-free summarization evaluation metric via centrality-weighted relevance and self-referenced redundancy
Wang Chen, Piji Li, and Irwin King. 2021 · 2021
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Understanding the extent to which content quality metrics measure the information quality of summaries
Daniel Deutsch and Dan Roth. 2021 · 2021
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SummEval: Re-evaluating summarization evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
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The GEM benchmark: Natural language generation, its evaluation and metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Mihir Kale, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa Prasad Majumder, Pedro Henrique Martins, Angelina McMillan-Major, Simon Mille, Emiel van Miltenburg, Moin Nadeem, Shashi Narayan, Vitaly Nikolaev, Andre Niyongabo Rubungo, Salomey Osei, Ankur Parikh, Laura Perez-Beltrachini, Niranjan Ramesh Rao, Vikas Raunak, Juan Diego Rodriguez, Sashank Santhanam, João Sedoc, Thibault Sellam, Samira Shaikh, Anastasia Shimorina, Marco Antonio Sobrevilla Cabezudo, Hendrik Strobelt, Nishant Subramani, Wei Xu, Diyi Yang, Akhila Yerukola, and Jiawei Zhou. 2021 · 2021
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Esin Durmus, Faisal Ladhak, and Tatsunori Hashimoto. 2022 · 2022
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How far are we from robust long abstractive summarization?
Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, and Shirui Pan. 2022 · 2022
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Revisiting the gold standard: Grounding summarization evaluation with robust human evaluation
Yixin Liu, Alexander R Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, et al. 2022 · 2022
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T5score: Discriminative fine-tuning of generative evaluation metrics
Yiwei Qin, Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2022 · 2022
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Layer or representation space: What makes BERT-based evaluation metrics robust?
Doan Nam Long Vu, Nafise Sadat Moosavi, and Steffen Eger. 2022 · 2022
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Gptscore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023 · 2023
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On the blind spots of model-based evaluation metrics for text generation
Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, and Yulia Tsvetkov. 2023 · 2023
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Shortcomings of question answering based factuality frameworks for error localization
Ryo Kamoi, Tanya Goyal, and Greg Durrett. 2023 · 2023
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G-eval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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Evaluating open-domain dialogues in latent space with next sentence prediction and mutual information
Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, and Xiaohui Cui. 2023 · 2023
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Towards a unified multi-dimensional evaluator for text generation
Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022 · 2038
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