{"url":"/dataset/glge","name":"GLGE","full_name":"General Language Generation Evaluation","description_markdown":"**GLGE** is a general language generation evaluation benchmark which is composed of 8 language generation tasks, including Abstractive Text Summarization ([CNN/DailyMail](cnn-daily-mail-1), Gigaword, [XSUM](xsum), MSNews), Answer-aware Question Generation ([SQuAD 1.1](squad), MSQG), Conversational Question Answering ([CoQA](coqa)), and Personalizing Dialogue ([Personachat](persona-chat-1)).","description_withheld":null,"homepage":"https://github.com/microsoft/glge","introduced_date":"2020-11-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/glge-a-new-general-language-generation","title":"GLGE: A New General Language Generation Evaluation Benchmark","first_author":"Dayiheng Liu","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Abstractive Text Summarization","url":"/task/abstractive-text-summarization","datasets_with_task":"/datasets/task/abstractive-text-summarization"},{"name":"Question Generation","url":"/task/question-generation","datasets_with_task":"/datasets/task/question-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["GLGE"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}