Dagan, Ido

Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference

Falke, T., Ribeiro, L. F. R., Utama, P. A., Dagan, I., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 265

Cross-document Coreference Resolution over Predicted Mentions

Cattan, A., Eirew, A., Stanovsky, G., Joshi, M., & Dagan, I. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Natural Language Processing TechniquesTopic Modeling

Cited by 29

Attribute First, then Generate: Locally-attributable Grounded Text Generation

Slobodkin, A., Hirsch, E., Cattan, A., Schuster, T., & Dagan, I. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 17

Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP

Goldman, O., Jacovi, A., Slobodkin, A., Maimon, A., Dagan, I., & Tsarfaty, R. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research

Hirsch, E., Wan, D., Wang, H., Stengel-Eskin, E., Bansal, M., & Dagan, I. (2026) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

GenerationPrograms: Fine-grained Attribution with Executable Programs

Wan, D., Hirsch, E., Stengel-Eskin, E., Dagan, I., & Bansal, M. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 0