CNN/Daily Mail is a dataset for text summarization. Human generated abstractive summary bullets were generated from news stories in CNN and Daily Mail websites as questions (with one of the entities hidden), and stories as the corresponding passages from which the system is expected to answer the fill-in the-blank question. The authors released the scripts that crawl, extract and generate pairs of passages and questions from these websites. In all, the corpus has 286,817 training pairs, 13,368 validation pairs and 11,487 test pairs, as defined by their scripts. The source documents in the training set have 766 words spanning 29.74 sentences on an average while the summaries consist of 53 words and 3.72 sentences. Source: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

GovReport is a dataset for long document summarization, with significantly longer documents and summaries. It consists of reports written by government research agencies including Congressional Research Service and U.S. Government Accountability Office. Compared with other long document summarization datasets, government report dataset has longer summaries and documents and requires reading in more context to cover salient words to be summarized.

The DUC2004 dataset is a dataset for document summarization. Is designed and used for testing only. It consists of 500 news articles, each paired with four human written summaries. Specifically it consists of 50 clusters of Text REtrieval Conference (TREC) documents, from the following collections: AP newswire, 1998-2000; New York Times newswire, 1998-2000; Xinhua News Agency (English version), 1996-2000. Each cluster contained on average 10 documents. Source: Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction Image Source: https://duc.nist.gov/duc2004/

DebateSum consists of 187328 debate documents, arguments (also can be thought of as abstractive summaries, or queries), word-level extractive summaries, citations, and associated metadata organized by topic-year. This data is ready for analysis by NLP systems. Source: https://github.com/Hellisotherpeople/DebateSum

SubSumE Dataset This repository contains the SubSumE dataset for subjective document summarization. See the paper and the talk for details on dataset creation. Also check out our work SuDocu on example-based document summarization. ## Dataset Files Download the dataset from here. The dataset contains : * Simplified text from 48 Wikipedia pages of the states in the US. Additionally, all the sentences in these documents are put together in a single file processed_state_sentences.csv and are assigned a unique sentence id that is used in summary json files. * Intent-based summaries created by human annotators. Each datapoint file in the directory user_summary_jsons contains a json containing summaries of Wikipedia pages of eight states with following keys: * intent : Summarization intent provided to human annotators for generating the summary * summaries: List of summary jsons for eight states assigned to the annotator. Each json in the list contains following keys: * state_name: Name of the state * sentence_ids: Global ids of sentences (wrt processed_state_sentences.csv) present in the summary * sentences: List of sentences present in the summary * use_keywords: Keywords used by the annotator to search the document when creating summaries