Abstractive Text Summarization

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

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

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: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

A new dataset with abstractive dialogue summaries. Source: SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

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. Source: WikiHow: A Large Scale Text Summarization Dataset Image Source: WikiHow: A Large Scale Text Summarization Dataset

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. Source: CORNELL NEWSROOM

The How2 dataset contains 13,500 videos, or 300 hours of speech, and is split into 185,187 training, 2022 development (dev), and 2361 test utterances. It has subtitles in English and crowdsourced Portuguese translations. Source: exploring multiview correlations in open-domain videos

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.

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics. This work is accepted by ACL findings 2021. You may find the paper here: https://arxiv.org/pdf/2105.06762.pdf. If you want to use our dataset, please cite our paper. #### Dialogue Data We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: * Under rich real-life scenarios, including more diverse task-oriented scenarios; * Have clear communication patterns and intents, which is valuable to serve as summarization sources; * Have a reasonable length, which comforts the purpose of automatic summarization. #### Summaries We ask annotators to summarize each dialogue based on the following criteria: * Convey the most salient information; * Be brief; * Preserve important named entities within the conversation; * Be written from an observer perspective; * Be written in formal language. #### Topics In addition to summaries, we also ask annotators to write a short topic for each dialogue, which can be potentially useful for future work, e.g. generating summaries by leveraging topic information. Image source: https://arxiv.org/pdf/2105.06762.pdf

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. Source: LCSTS: A Large Scale Chinese Short Text Summarization Dataset

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. Source: Generating Wikipedia by Summarizing Long Sequences Image Source: https://arxiv.org/pdf/1801.10198.pdf

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. Source: https://github.com/esdurmus/Wikilingua Image Source: Ladhak et al

BillSum is the first dataset for summarization of US Congressional and California state bills. The BillSum dataset consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the Govinfo service provided by the United States Government Publishing Office (GPO). The corpus consists of bills from the 103rd-115th (1993-2018) sessions of Congress. The data was split into 18,949 train bills and 3,269 test bills. For California, bills from the 2015-2016 session were scraped directly from the legislature’s website; the summaries were written by their Legislative Counsel. The BillSum corpus focuses on mid-length legislation from 5,000 to 20,000 character in length. The authors chose to measure the text length in characters, instead of words or sentences, because the texts have complex structure that makes it difficult to consistently measure words. The range was chosen because on one side, short bills introduce minor changes and do not require summaries. While the CRS produces summaries for them, they often contain most of the text of the bill. On the other side, very long legislation is often composed of several large sections. Source: BillSum: A Corpus for Automatic Summarization of US Legislation

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. Source: https://github.com/ctr4si/MMN

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. Source: MLSUM: The Multilingual Summarization Corpus

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. BookSum contains summaries for 142,753 paragraphs, 12,293 chapters and 436 books.

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. Source: https://arxiv.org/pdf/1808.08745.pdf Image Source: https://arxiv.org/pdf/1808.08745.pdf

This dataset contains 4,828 full biomedical articles paired with non-technical lay summaries derived from the eLife scientific journal.

To study the task of email subject line generation: automatically generating an email subject line from the email body. Source: This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation

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. Each AMR is a single rooted, directed graph. AMRs include PropBank semantic roles, within-sentence coreference, named entities and types, modality, negation, questions, quantities, and so on. The image presents an AMR of a sample sentence “The boy wants to go”. Source: https://amr.isi.edu/ Image Source: https://amr.isi.edu/language.html

This dataset contains 27,525 full biomedical articles paired with non-technical lay summaries derived from various journals published by the Public Library of Science (PLOS).

This is a dataset for evaluating summarisation methods for research papers. Source: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

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. Source: TLDR: Extreme Summarization of Scientific Documents

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).

5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl. Source: AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization

The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. Source: Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset

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. Source: Global Voices: Crossing Borders in Automatic News Summarization

Presents two high-quality large-scale CLS datasets based on existing monolingual summarization datasets. Source: NCLS: Neural Cross-Lingual Summarization

ConvoSumm is a suite of four datasets to evaluate a model’s performance on a broad spectrum of conversation data.

A set of approximately 100K podcast episodes comprised of raw audio files along with accompanying ASR transcripts. This represents over 47,000 hours of transcribed audio, and is an order of magnitude larger than previous speech-to-text corpora. Source: The Spotify Podcast Dataset

A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs. Source: Liputan6: A Large-scale Indonesian Dataset for Text Summarization

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: • Sarcasm source: The speaker in the dialog who is being sarcastic. • Sarcasm target: The person/ thing towards whom the sarcasm is directed. • Action word: Verb/ action used to describe how the sarcasm is taking place. e.g. mocking, insults, taunts, etc. • Description: A description of the scene to help contextualize the sarcasm.

Pn-summary is a dataset for Persian abstractive text summarization. Source: https://arxiv.org/abs/2012.11204 Image Source: https://github.com/hooshvare/pn-summary

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.

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. Source: NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization Image Source: https://arxiv.org/pdf/2212.01476v1.pdf

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. Source: Shmoop Corpus