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Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization.

Y. Liu, A. Fabbri, J. Chen, Y. Zhao, S. Han, S. Joty, P. Liu, D. Radev, C. Wu, and A. Cohan. CoRR, (2023)

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Mark R Alexander

Alexander R Markus

Alexander R Rohr

Alexander R Frauer

R Chaudhury

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QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization.A. Fabbri, C. Wu, W. Liu, and C. Xiong. NAACL-HLT, page 2587-2601. Association for Computational Linguistics, (2022)What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning.I. Li, A. Fabbri, R. Tung, and D. Radev. AAAI, page 6674-6681. AAAI Press, (2019)Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors.L. Tang, T. Goyal, A. Fabbri, P. Laban, J. Xu, S. Yavuz, W. Kryscinski, J. Rousseau, and G. Durrett. ACL (1), page 11626-11644. Association for Computational Linguistics, (2023)Generating EDU Extracts for Plan-Guided Summary Re-Ranking.G. Adams, A. Fabbri, F. Ladhak, N. Elhadad, and K. McKeown. ACL (1), page 2680-2697. Association for Computational Linguistics, (2023)Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation.Y. Liu, A. Fabbri, P. Liu, Y. Zhao, L. Nan, R. Han, S. Han, S. Joty, C. Wu, C. Xiong and 1 other author(s). CoRR, (2022)Exploring Neural Models for Query-Focused Summarization.J. Vig, A. Fabbri, W. Kryscinski, C. Wu, and W. Liu. NAACL-HLT (Findings), page 1455-1468. Association for Computational Linguistics, (2022)Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries.X. Tang, A. Fabbri, H. Li, Z. Mao, G. Adams, B. Wang, A. Celikyilmaz, Y. Mehdad, and D. Radev. NAACL-HLT, page 5680-5692. Association for Computational Linguistics, (2022)R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning.I. Li, A. Fabbri, S. Hingmire, and D. Radev. COLING, page 1147-1157. International Committee on Computational Linguistics, (2020)Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering.A. Fabbri, P. Ng, Z. Wang, R. Nallapati, and B. Xiang. ACL, page 4508-4513. Association for Computational Linguistics, (2020)Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations.R. Zhang, C. Westerfield, S. Shim, G. Bingham, A. Fabbri, W. Hu, N. Verma, and D. Radev. ACL (1), page 3173-3179. Association for Computational Linguistics, (2019)

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Disambiguation of "Fabbri, Alexander R."

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Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization.

Please choose a person to relate this publication to

Mark R Alexander

Alexander R Markus

Alexander R Rohr

Alexander R Frauer

R Chaudhury

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Fabbri, Alexander R."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization.

Please choose a person to relate this publication to

Mark R Alexander

Alexander R Markus

Alexander R Rohr

Alexander R Frauer

R Chaudhury

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
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URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization.