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cs.CL2022
Evaluating and Improving Factuality in Multimodal Abstractive Summarization
David Wan, Mohit Bansal
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modalit…
cs.CL2022★ 1 cited
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization
Shiyue Zhang, David Wan, Mohit Bansal
The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithf…
cs.CL2022★ 1 cited
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
David Wan, Mohit Bansal
We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strat…