FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual
Robustness

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness

1 November 2022

Sujian Li

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Papers citing "FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness"

5 / 5 papers shown

Title
AdvSumm: Adversarial Training for Bias Mitigation in Text Summarization Mukur Gupta Nikhil Reddy Varimalla Nicholas Deas Melanie Subbiah Kathleen McKeown 57 0 0 06 Jun 2025
Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency Taiji Li Zhi Li Yin Zhang HILM 124 11 0 31 Jul 2024
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models Jongyoon Song Nohil Park Bongkyu Hwang Jaewoong Yun Seongho Joe Youngjune Gwon Sungroh Yoon KELM HILM 77 1 0 23 Feb 2024
Effective Slogan Generation with Noise Perturbation Jongeun Kim MinChung Kim Taehwan Kim VLM 37 1 0 06 Oct 2023
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning Wenhao Wu Wei Li Xinyan Xiao Jiachen Liu Sujian Li Yajuan Lv HILM 102 6 0 20 Dec 2022