Annotated Gigaword

By Courtney Napoles, Matthew R. Gormley, Benjamin Van Durme. 2012

376 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Unsupervised Pretraining for Sequence to Sequence Learning (2016, methodology): “In this setting, we used the English Gigaword corpus (Napoles et al., 2012) as our larger, unlabeled “monolingual” corpus, although all data used in this task is in English.”
Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, methodology): “One of the suggestions by Søgaard et al. (2021) is to simulate dataset shifts to simulate a more realistic deployment scenario, which they test on the Gigaword sentence compression dataset (Napoles et al., 2012) where they divide splits by year of publication.”
Finetuned Language Models Are Zero-Shot Learners (2021, neutral): “Gigaword (Napoles et al., 2012) 4.”
MLSUM: The Multilingual Summarization Corpus (2020, methodology): “However, due to the small number of training data available, DUC datasets are often used in a domain adaptation setup for models ﬁrst trained on larger datasets such as Gigaword, CNN/DM (Nallapati et al., 2016; See et al., 2017) or with unsupervised methods (Dorr et al., 2003; Mihalcea and Tarau, 20”
CM3: A Causal Masked Multimodal Model of the Internet (2022, background): “We look at the same datasets as HTLM. Gigaword consists of headlines from news articles (Napoles et al., 2012).”
Hypothesis Only Baselines in Natural Language Inference (2018, methodology): “Premise sentences were extracted from Annotated Gigaword (Napoles et al., 2012), image captions (Young et al., 2014), the Internet Argument Corpus (Walker et al., 2012), and ﬁc-tional stories from the GutenTag dataset (Mac Kim and Cassidy, 2015).”
A Neural Attention Model for Abstractive Sentence Summarization (2015, methodology): “For training data for both tasks, we utilize the annotated Gigaword data set (Graff et al., 2003; Napoles et al., 2012), which consists of standard Gigaword, preprocessed with Stanford CoreNLP tools (Manning et al., 2014).”
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies (2018, methodology): “This includes using news head-lines as summaries of article preﬁxes (Napoles et al., 2012; Rush et al., 2015), concatenating bullet points as summaries (Hermann et al., 2015; See et al., 2017), or using librarian archival summaries (Sandhaus, 2008).”
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks (2016, methodology): “Our models are trained on the annotated version of the Gigaword corpus (Graff et al., 2003; Napoles et al., 2012) and we use only the annotations for tokenization and sentence separation while discarding other annotations such as tags and parses.”
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (2019, methodology): “87B wordpieces, 13GB of plain text) and XLNet additionally on Giga5 (Napoles et al., 2012), ClueWeb 2012-B (extended from Smucker et al.”
Direct Output Connection for a High-Rank Language Model (2018, background): “Rush et al. (2015) constructed a headline generation dataset by extracting pairs of ﬁrst sentences of news articles and their headlines from the annotated English Gigaword corpus (Napoles et al., 2012).”
Image Description using Visual Dependency Representations (2013, methodology): “At training time, the model has access to the annotated image regions and labels, and to the dependency-parsed version of the English Gigaword Corpus (Napoles et al., 2012).”
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018, background): “News articles are exploited the most, including in DUC (Over et al., 2007), Giga-word (Napoles et al., 2012), CNN/DailyMail (Nal-lapati et al., 2016; Hermann et al., 2015), News-room (Grusky et al., 2018) and XSum (Narayan et al., 2018a) datasets.”
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2019, methodology): “and abstractive summarization using three datasets: Gigaword (Napoles et al., 2012), CNN and”
Controlling Output Length in Neural Encoder-Decoders (2016, methodology): “We trained our models on a part of the Annotated English Gigaword corpus (Napoles et al., 2012), which Rush et al.”