Factual Error Correction for Abstractive Summaries Using Entity Retrieval

18 April 2022

Papers citing "Factual Error Correction for Abstractive Summaries Using Entity Retrieval"

6 / 6 papers shown

Title
Factual Dialogue Summarization via Learning from Large Language Models Rongxin Zhu Jey Han Lau Jianzhong Qi HILM 55 1 0 20 Jun 2024
Envisioning the Next-Gen Document Reader Catherine Yeh Nedim Lipka Franck Dernoncourt SyDa 20 0 0 15 Feb 2023
Improving Factual Consistency in Summarization with Compression-Based Post-Editing Alexander R. Fabbri Prafulla Kumar Choubey Jesse Vig Chien-Sheng Wu Caiming Xiong HILM KELM 44 17 0 11 Nov 2022
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics Artidoro Pagnoni Vidhisha Balachandran Yulia Tsvetkov HILM 233 306 0 27 Apr 2021
Factual Error Correction for Abstractive Summarization Models Mengyao Cao Yue Dong Jiapeng Wu Jackie C.K. Cheung HILM KELM 169 159 0 17 Oct 2020
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents Ramesh Nallapati Feifei Zhai Bowen Zhou 207 1,255 0 14 Nov 2016