Xsum Paper, For both BART and Pegasus, SFT produces distilled mod

Xsum Paper, For both BART and Pegasus, SFT produces distilled models that are 75% faster XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it. On CNN, SFT outperforms the more expensive methods. - summary: One sentence summary of the article. Below are a number of evaluation datasets which were developed in This repository contains the faithfulness and factuality annotations of XSum summaries from our paper "On Faithfulness and Factuality in Abstractive Summarization" at ACL 2020. Topic-Aware Convolutional Neural Networks for Extreme Summarization, by Shashi Narayan and Shay B. Given the unique characteristics of CNN/DM and XSum, our proposed benchmark includes two subsets, AGGREFACT Topic-Aware Convolutional Neural Networks for Extreme Summarization - XSum/README. XSum Hallucination Annotations This repository contains the faithfulness and factuality annotations of XSum summaries from our paper "On Faithfulness and XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it. This provides a use-ful point of comparison between task-specific fine-tuned Exactly-rounded summation of floating point values - xsum/xsum-paper. You can load the dataset via: The data loader can be found here. The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query.

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