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FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual
  Robustness

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness

1 November 2022
Wenhao Wu
Wei Li
Jiachen Liu
Xinyan Xiao
Ziqiang Cao
Sujian Li
Hua Wu
    HILM
ArXiv (abs)PDFHTML

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
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
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
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
KELMHILM
77
1
0
23 Feb 2024
Effective Slogan Generation with Noise Perturbation
Effective Slogan Generation with Noise Perturbation
Jongeun Kim
MinChung Kim
Taehwan Kim
VLM
39
1
0
06 Oct 2023
WeCheck: Strong Factual Consistency Checker via Weakly Supervised
  Learning
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
Wenhao Wu
Wei Li
Xinyan Xiao
Jiachen Liu
Sujian Li
Yajuan Lv
HILM
104
6
0
20 Dec 2022
1