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Found 5 papers, 5 papers with code
Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis
After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation.
Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments.
FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction
To address these shortcomings, we propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE), a more interpretable and efficient factuality-oriented metric.
Ranked #1 on
Summarization Consistency Evaluation
on AggreFact
Natural Language Inference
Summarization Consistency Evaluation
Echoes from Alexandria: A Large Resource for Multilingual Book Summarization
In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features.
Ranked #1 on
Text Summarization
on BookSum
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.
