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We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature.
Markov processes over denumerable products of spaces, describing large systems of automata
Leonid Nisonovich Vaserstein. 1969 · 1969
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Revisiting summarization evaluation for scientific articles
Arman Cohan and Nazli Goharian. 2016 · 2016
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Detecting opinion spams and fake news using text classification
Hadeer Ahmed, Issa Traore, and Sherif Saad. 2018 · 2018
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Abstractive summarization of reddit posts with multi-level memory networks
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2018 · 2018
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Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
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Countering the effects of lead bias in news summarization via multi-stage training and auxiliary losses
Matt Grenander, Yue Dong, Jackie Chi Kit Cheung, and Annie Louis. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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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.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
Cited alongside, same era.
An overview of fairness in clustering
Anshuman Chhabra, Karina Masalkovaitė, and Prasant Mohapatra. 2021 · 2021
Cited alongside, same era.
Demoting the lead bias in news summarization via alternating adversarial learning
Linzi Xing, Wen Xiao, and Giuseppe Carenini. 2021 · 2021
Cited alongside, same era.
Leveraging lead bias for zero-shot abstractive news summarization
Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, and Xuedong Huang. 2021 · 2021
Cited alongside, same era.
Better zero-shot reasoning with role-play prompting
Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, and Xin Zhou. 2023 · 2023
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On position bias in summarization with large language models
Mathieu Ravaut, Shafiq Joty, Aixin Sun, and Nancy F Chen. 2023 · 2023
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The current state of summarization
Fabian Retkowski. 2023 · 2023
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Are large language models good evaluators for abstractive summarization?
Chenhui Shen, Liying Cheng, Yang You, and Lidong Bing. 2023 · 2023
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Evaluating the factual consistency of large language models through news summarization
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Anshuman Chhabra, Ashwin Sekhari, and Prasant Mohapatra. 2022a
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Benchmarking large language models for news summarization
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