Interpretable Automatic Fine-grained Inconsistency Detection in Text
Summarization

Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization

23 May 2023

Papers citing "Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization"

9 / 9 papers shown

Title
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models Kung-Hsiang Huang Hou Pong Chan Yi Ren Fung Haoyi Qiu Mingyang Zhou Shafiq R. Joty Shih-Fu Chang Heng Ji AI4TS 64 14 0 18 Mar 2024
Identifying Factual Inconsistencies in Summaries: Grounding Model Inference via Task Taxonomy Liyan Xu Zhenlin Su Mo Yu Jin Xu Jinho D. Choi Jie Zhou Fei Liu HILM 21 2 0 20 Feb 2024
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation Haoyi Qiu Kung-Hsiang Huang Jingnong Qu Nanyun Peng HILM 21 6 0 16 Nov 2023
Defining a New NLP Playground Sha Li Chi Han Pengfei Yu Carl N. Edwards Manling Li ... Yi Ren Fung Charles Yu Joel R. Tetreault Eduard H. Hovy Heng Ji 31 5 0 31 Oct 2023
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media Kung-Hsiang Huang Hou Pong Chan Kathleen McKeown Heng Ji 12 1 0 23 May 2023
Zero-shot Faithful Factual Error Correction Kung-Hsiang Huang Hou Pong Chan Heng Ji KELM HILM 19 30 0 13 May 2023
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization C. Cheang Hou Pong Chan Derek F. Wong Xuebo Liu Zhao Li Yanming Sun Shudong Liu Lidia S. Chao 186 6 0 03 May 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 42 17 0 11 Nov 2022
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