Unsupervised Extractive Summarization

The PubMed dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. Source: https://linqs.soe.ucsc.edu/data

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

FacetSum is a faceted summarization dataset for scientific documents. FacetSum has been built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value.

We provide a new data set XWikiRef for the task of Cross-lingual Multi-document Summarization. This task aims at generating Wikipedia style text in Low Resource languages by taking reference text as input. Overall, the data set contains 8 different languages: bengali (bn), english (en), hindi (hi), marathi (mr), malayalam (ml), odia (or), punjabi (pa) and tamil (ta). It also contains 5 domains: books, films, politicians, sportsman and writers. ##Data Format Dataset is publicly available here. Each directory contains language specific data subset having 1 json file per domain. In each file, each line denotes one article. It contains the following set of keys: - Article title - Sections - section title 1 - section text 1 - list of reference texts 1 - ..... - ..... - ..... - section title n - section text n - list of reference texts 1