factual consistency

CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization EMNLP 2021 Discourse Understanding and Factual Consistency in Abstractive Summarization EACL 2021 Enhancing Factual Consistency of Abstractive Summarization NAACL 2021 Entity-level Factual Consistency of Abstractive Text Summarization EACL 2021 Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation EMNLP 2021 Fine-grained Factual Consistency Assessment for Abstractive Summarization Models EMNLP 2021 Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization EMNLP 2021 Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation NAACL 2021 Improving Factual Consistency Between a Response and Persona Facts EACL 2021 Improving Factual Consistency of Abstractive Summarization on Customer Feedback ACL 2021 Improving Factual Consistency of Abstractive Summarization via Question Answering ACL 2021 Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering EMNLP 2021 SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization ACL 2021 Using Question Answering Rewards to Improve Abstractive Summarization EMNLP 2021 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries ACL 2020 Evaluating the Factual Consistency of Abstractive Text Summarization EMNLP 2020 Factual Error Correction for Abstractive Summarization Models EMNLP 2020 Multi-Fact Correction in Abstractive Text Summarization EMNLP 2020 What Have We Achieved on Text Summarization? EMNLP 2020