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Questioning the Validity of Summarization Datasets and Improving Their
  Factual Consistency

Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency

31 October 2022
Yanzhu Guo
Chloé Clavel
Moussa Kamal Eddine
Michalis Vazirgiannis
    HILM
ArXivPDFHTML

Papers citing "Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency"

6 / 6 papers shown
Title
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data
  Annotation
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Juhwan Choi
Jungmin Yun
Kyohoon Jin
Youngbin Kim
30
4
0
15 Apr 2024
The Factual Inconsistency Problem in Abstractive Text Summarization: A
  Survey
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Yi-Chong Huang
Xiachong Feng
Xiaocheng Feng
Bing Qin
HILM
128
104
0
30 Apr 2021
Understanding Factuality in Abstractive Summarization with FRANK: A
  Benchmark for Factuality Metrics
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Artidoro Pagnoni
Vidhisha Balachandran
Yulia Tsvetkov
HILM
215
305
0
27 Apr 2021
Entity-level Factual Consistency of Abstractive Text Summarization
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan
Ramesh Nallapati
Zhiguo Wang
Cicero Nogueira dos Santos
Henghui Zhu
Dejiao Zhang
Kathleen McKeown
Bing Xiang
HILM
142
157
0
18 Feb 2021
The GEM Benchmark: Natural Language Generation, its Evaluation and
  Metrics
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann
Tosin P. Adewumi
Karmanya Aggarwal
Pawan Sasanka Ammanamanchi
Aremu Anuoluwapo
...
Nishant Subramani
Wei-ping Xu
Diyi Yang
Akhila Yerukola
Jiawei Zhou
VLM
246
283
0
02 Feb 2021
Teaching Machines to Read and Comprehend
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
Tomás Kociský
Edward Grefenstette
L. Espeholt
W. Kay
Mustafa Suleyman
Phil Blunsom
170
3,508
0
10 Jun 2015
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