Investigating Crowdsourcing Protocols for Evaluating the Factual
Consistency of Summaries

Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries

19 September 2021

Alexander R. Fabbri

Asli Celikyilmaz

Yashar Mehdad

Dragomir R. Radev

Papers citing "Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries"

8 / 8 papers shown

Title
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey Yi-Chong Huang Xiachong Feng Xiaocheng Feng Bing Qin HILM 148 107 0 30 Apr 2021
GO FIGURE: A Meta Evaluation of Factuality in Summarization Saadia Gabriel Asli Celikyilmaz Rahul Jha Yejin Choi Jianfeng Gao HILM 253 96 0 24 Oct 2020
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization Jingqing Zhang Yao-Min Zhao Mohammad Saleh Peter J. Liu RALM 3DGS 191 2,029 0 18 Dec 2019
Evaluating the Factual Consistency of Abstractive Text Summarization Wojciech Kry'sciñski Bryan McCann Caiming Xiong R. Socher HILM 69 739 0 28 Oct 2019
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong Nan Yang Wenhui Wang Furu Wei Xiaodong Liu Yu Wang Jianfeng Gao M. Zhou H. Hon ELM AI4CE 150 1,553 0 08 May 2019
MASS: Masked Sequence to Sequence Pre-training for Language Generation Kaitao Song Xu Tan Tao Qin Jianfeng Lu Tie-Yan Liu 92 962 0 07 May 2019
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization Shashi Narayan Shay B. Cohen Mirella Lapata AILaw 102 1,652 0 27 Aug 2018
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation S. Kiritchenko Saif M. Mohammad 40 177 0 05 Dec 2017