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AMRFact: Enhancing Summarization Factuality Evaluation with AMR-driven Training Data Generation.

, , , and . CoRR, (2023)

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AMRFact: Enhancing Summarization Factuality Evaluation with AMR-driven Training Data Generation., , , and . CoRR, (2023)The Battlefront of Combating Misinformation and Coping with Media Bias., , , and . KDD, page 4790-4791. ACM, (2022)From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models., , , , , , , and . CoRR, (2024)Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles., , , , , , and . NAACL-HLT, page 570-593. Association for Computational Linguistics, (2024)A-HA: a hybrid approach for hotel recommendation., , , , , , and . RecSys Challenge, page 4:1-4:5. ACM, (2019)Cross-document Misinformation Detection based on Event Graph Reasoning., , , and . NAACL-HLT, page 543-558. Association for Computational Linguistics, (2022)Zero-shot Faithful Factual Error Correction., , and . ACL (1), page 5660-5676. Association for Computational Linguistics, (2023)CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval., , and . COLING, page 1024-1035. International Committee on Computational Linguistics, (2022)Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation., , , , and . ACL (1), page 14571-14589. Association for Computational Linguistics, (2023)Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning., , , , , , , and . ACL (Findings), page 730-749. Association for Computational Linguistics, (2024)