Multi-News, consists of news articles and human-written summaries of these articles from the site newser.com. Each summary is professionally written by editors and includes links to the original articles cited.
103 PAPERS • 4 BENCHMARKS
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.
51 PAPERS • NO BENCHMARKS YET
The WCEP dataset for multi-document summarization (MDS) consists of short, human-written summaries about news events, obtained from the Wikipedia Current Events Portal (WCEP), each paired with a cluster of news articles associated with an event. These articles consist of sources cited by editors on WCEP, and are extended with articles automatically obtained from the Common Crawl News dataset.
29 PAPERS • 1 BENCHMARK
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.
15 PAPERS • 4 BENCHMARKS
5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.
10 PAPERS • NO BENCHMARKS YET
OPOSUM is a dataset for the training and evaluation of Opinion Summarization models which contains Amazon reviews from six product domains: Laptop Bags, Bluetooth Headsets, Boots, Keyboards, Televisions, and Vacuums. The six training collections were created by downsampling from the Amazon Product Dataset introduced in McAuley et al. (2015) and contain reviews and their respective ratings.
8 PAPERS • NO BENCHMARKS YET
Multi-XScience is a large-scale dataset for multi-document summarization of scientific articles. It has 30,369, 5,066 and 5,093 samples for the train, validation and test split respectively. The average document length is 778.08 words and the average summary length is 116.44 words.
6 PAPERS • NO BENCHMARKS YET
Maternal and Infant (MATINF) Dataset is a large-scale dataset jointly labeled for classification, question answering and summarization in the domain of maternity and baby caring in Chinese. An entry in the dataset includes four fields: question (Q), description (D), class (C) and answer (A).
5 PAPERS • NO BENCHMARKS YET
Wikipedia Generation is a dataset for article generation from Wikipedia from references at the end of Wikipedia page and the top 10 search results for the Wikipedia topic.
4 PAPERS • NO BENCHMARKS YET
Healthline is a nutrition related dataset for multi-document summarization, using scientific studies.
3 PAPERS • NO BENCHMARKS YET
MS^2 (Multi-Document Summarization of Medical Studies) is a dataset of over 470k documents and 20k summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is one of the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.
3 PAPERS • 1 BENCHMARK
OpenAsp Dataset OpenAsp is an Open Aspect-based Multi-Document Summarization dataset derived from DUC and MultiNews summarization datasets.
2 PAPERS • NO BENCHMARKS YET
This is a dataset for multi-document summarization in Portuguese, what means that it has examples of multiple documents (input) related to human-written summaries (output). In particular, it has entries of multiple related texts from Brazilian websites about a subject, and the summary is the Portuguese Wikipedia lead section on the same subject (lead: the first section, i.e., summary, of any Wipedia article). Input texts were extracted from BrWac corpus, and the output from Brazilian Wikipedia dumps page.
1 PAPER • NO BENCHMARKS YET
GameWikiSum is a domain-specific (video game) dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news. Input documents consist of long professional video game reviews as well as references of their gameplay sections in Wikipedia pages.
In an active e-commerce environment, customers process a large number of reviews when deciding on whether to buy a product or not. Abstractive Multi-Review Summarization aims to assist users to efficiently consume the reviews that are the most relevant to them. We propose the first large-scale abstractive multi-review summarization dataset that leverages more than 17.9 billion raw reviews and uses novel aspect-alignment techniques based on aspect annotations. Furthermore, we demonstrate that one can generate higher-quality review summaries by using a novel aspect-alignment-based model. Results from both automatic and human evaluation show that the proposed dataset plus the innovative aspect-alignment model can generate high-quality and trustful review summaries.
0 PAPER • NO BENCHMARKS YET