8 citations · 14 across the 7 of their papers we have counts for
6 papers · 1 filter
Calibrating Likelihoods towards Consistency in Summarization Models
Polina Zablotskaia, Misha Khalman, Rishabh Joshi +4
Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility fo…
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot, Joshua Maynez, Shashi Narayan +6
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelev…
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Polina Zablotskaia, Du Phan, Joshua Maynez +3
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign hi…
SMART: Sentences as Basic Units for Text Evaluation
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao +1
Widely used evaluation metrics for text generation either do not work well with longer texts or fail to evaluate all aspects of text quality. In this paper, we introduce a new metr…
What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks
Shashi Narayan, Shay B. Cohen, Mirella Lapata
We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the articl…
Split and Rephrase
Shashi Narayan, Claire Gardent, Shay B. Cohen +1
We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like senten…