182 citations · 191 across the 11 of their papers we have counts for
4 papers · 1 filter
Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
Ryo Kamoi, Tanya Goyal, Greg Durrett
Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most p…
News Summarization and Evaluation in the Era of GPT-3
Tanya Goyal, Junyi Jessy Li, Greg Durrett
The recent success of prompting large language models like GPT-3 has led to a paradigm shift in NLP research. In this paper, we study its impact on text summarization, focusing on…
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Liyan Tang, Tanya Goyal, Alexander R. Fabbri +6
The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of error…
SNaC: Coherence Error Detection for Narrative Summarization
Tanya Goyal, Junyi Jessy Li, Greg Durrett
Progress in summarizing long texts is inhibited by the lack of appropriate evaluation frameworks. When a long summary must be produced to appropriately cover the facets of that tex…