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.
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The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes:
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A new dataset with abstractive dialogue summaries.
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WikiHow is a dataset of more than 230,000 article and summary pairs extracted and constructed from an online knowledge base written by different human authors. The articles span a wide range of topics and represent high diversity styles.
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CORNELL NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. The summaries are obtained from search and social metadata between 1998 and 2017 and use a variety of summarization strategies combining extraction and abstraction.
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LCSTS is a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. The authors also manually tagged the relevance of 10,666 short summaries with their corresponding short texts 10,666 short summaries with their corresponding short texts.
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WikiSum is a dataset based on English Wikipedia and suitable for a task of multi-document abstractive summarization. In each instance, the input is comprised of a Wikipedia topic (title of article) and a collection of non-Wikipedia reference documents, and the target is the Wikipedia article text. The dataset is restricted to the articles with at least one crawlable citation. The official split divides the articles roughly into 80/10/10 for train/development/test subsets, resulting in 1865750, 233252, and 232998 examples respectively.
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WikiLingua includes ~770k article and summary pairs in 18 languages from WikiHow. Gold-standard article-summary alignments across languages are extracted by aligning the images that are used to describe each how-to step in an article.
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XL-Sum is a comprehensive and diverse dataset for abstractive summarization comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.
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DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics.
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Reddit TIFU dataset is a newly collected Reddit dataset, where TIFU denotes the name of /r/tifu subbreddit. There are 122,933 text-summary pairs in total.
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A large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
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BillSum is the first dataset for summarization of US Congressional and California state bills.
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BookSum is a collection of datasets for long-form narrative summarization. This dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of this dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures.
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The Extreme Summarization (XSum) dataset is a dataset for evaluation of abstractive single-document summarization systems. The goal is to create a short, one-sentence new summary answering the question “What is the article about?”. The dataset consists of 226,711 news articles accompanied with a one-sentence summary. The articles are collected from BBC articles (2010 to 2017) and cover a wide variety of domains (e.g., News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts). The official random split contains 204,045 (90%), 11,332 (5%) and 11,334 (5) documents in training, validation and test sets, respectively.
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The AMR Bank is a set of English sentences paired with simple, readable semantic representations. Version 3.0 released in 2020 consists of 59,255 sentences.
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GLGE is a general language generation evaluation benchmark which is composed of 8 language generation tasks, including Abstractive Text Summarization (CNN/DailyMail, Gigaword, XSUM, MSNews), Answer-aware Question Generation (SQuAD 1.1, MSQG), Conversational Question Answering (CoQA), and Personalizing Dialogue (Personachat).
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A new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
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This is a dataset for evaluating summarisation methods for research papers.
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Global Voices is a multilingual dataset for evaluating cross-lingual summarization methods. It is extracted from social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages.
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ConvoSumm is a suite of four datasets to evaluate a model’s performance on a broad spectrum of conversation data.
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This dataset is an extension of MASAC, a multimodal, multi-party, Hindi-English code-mixed dialogue dataset compiled from the popular Indian TV show, ‘Sarabhai v/s Sarabhai’. WITS was created by augmenting MASAC with natural language explanations for each sarcastic dialogue. The dataset consists of the transcribed sarcastic dialogues from 55 episodes of the TV show, along with audio and video multimodal signals. It was designed to facilitate Sarcasm Explanation in Dialogue (SED), a novel task aimed at generating a natural language explanation for a given sarcastic dialogue, that spells out the intended irony. Each data instance in WITS is associated with a corresponding video, audio, and textual transcript where the last utterance is sarcastic in nature. All the final selected explanations contain the following attributes:
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The DeepMind Q&A Dataset consists of two datasets for Question Answering, CNN and DailyMail. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.
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Shmoop Corpus is a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, a set of common NLP tasks are constructed, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories.
FINDSum is a large-scale dataset for long text and multi-table summarization. It is built on 21,125 annual reports from 3,794 companies and has two subsets for summarizing each company’s results of operations and liquidity.
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PeerSum is a new MDS dataset using peer reviews of scientific publications. The dataset differs from the existing MDS datasets in that summaries (i.e., the meta-reviews) are highly abstractive and they are real summaries of the source documents.
Pn-summary is a dataset for Persian abstractive text summarization.
The Gigaword Entailment dataset is a dataset for entailment prediction between an article and its headline. It is built from the Gigaword dataset.
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A maintained database tracks ICLR submissions and reviews, augmented with author profiles and higher-level textual features.
NarraSum is a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries.
This is a large-scale court judgment dataset, where each judgment is a summary of the case description with a patternized style. It contains 2,003,390 court judgment documents. The case description is used as the input, and the court judgment as the summary. The average lengths of the input documents and summaries are 595.15 words and 273.57 words respectively.
This corpus contains preprocessed posts from the Reddit dataset, suitable for abstractive summarization using deep learning. The format is a json file where each line is a JSON object representing a post. The schema of each post is shown below: - author: string (nullable = true) - body: string (nullable = true) - normalizedBody: string (nullable = true) - content: string (nullable = true) - content_len: long (nullable = true) - summary: string (nullable = true) - summary_len: long (nullable = true) - id: string (nullable = true) - subreddit: string (nullable = true) - subreddit_id: string (nullable = true) - title: string (nullable = true)