- 21.12.18 GO FIGURE: A Meta Evaluation of Factuality in Summarization
- 21.12.1 Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
- 21.12.1 Inspecting the Factuality of Hallucinated Entities in Abstractive Summarization
- 21.12.2 Annotating and Modeling Fine-grained Factuality in Summarization
- 21.12.2 Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
- 21.12.9 Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
- 21.12.11 Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection 【NAACL2021】
- 21.12.15 QuestEval: Summarization Asks for Fact-based Evaluation
- 21.12.15 Enhancing Factual Consistency of Abstractive Summarization
- 21.12.16 Assessing The Factual Accuracy of Generated Text
- 21.12.17 Detecting Hallucinated Content in Conditional Neural Sequence Generation
- 21.12.17 Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
- 21.12.18 Truth or Error? Towards systematic analysis of factual errors in abstractive summaries
- 21.12.30 Robust Neural Machine Translation with Doubly Adversarial Inputs
- 21.12.18 Reducing Quantity Hallucinations in Abstractive Summarization【EMNLP20】
- 21.12.18 Multi-Fact Correction in Abstractive Text Summarization.
- 21.12.19 Improving Truthfulness of Headline Generation
- 21.12.20 Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization [NIPS19]
- 21.12.20 Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
- 21.12.20 Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
- 21.12.20 FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
- 21.12.20 Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
- 21.12.20 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
- 21.12.27 反事实
- 21.12.30 Towards a Universal Continuous Knowledge Base 连续性知识库