A Large-Scale Multi-Document Summarization Dataset from the W ikipedia Current Events Portal (2020)

Abstract: Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation. However, there is a lack of datasets that realistically address such use cases at a scale large enough for training supervised models for this task. This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters. We build this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which provides concise and neutral human-written summaries of news events, with links to external source articles. We also automatically extend these source articles by looking for related articles in the Common Crawl archive. We provide a quantitative analysis of the dataset and empirical results for several state-of-the-art MDS techniques.

## introduction text summarization has recently received increased attention with the rise of deep learning-based endto-end models, both for extractive and abstractive variants. however, so far, only single-document summarization has profited from this trend. multidocument summarization (mds) still suffers from a lack of established large-scale datasets. this impedes the use of large deep learning models, which have greatly improved the state-of-the-art for various supervised nlp problems @xcite @xcite , and makes a robust evaluation difficult. recently, several larger mds datasets have been created: @xcite ; @xcite ; @xcite . however, these datasets do not realistically resemble use human-written summary emperor akihito abdicates the chrysanthemum throne in favor of his elder son, crown prince naruhito. he is the first emperor to abdicate in over two hundred years, since emperor kökaku in 1817. headlines of source articles (wcep) in this paper, we present the wikipedia current events portal (wcep) dataset, which is designed to address real-world mds use cases. the dataset consists of 10,200 clusters with one human-written summary and 235 articles per cluster on average. we extract this dataset starting from the wikipedia current events portal (wcep) foot_0 . editors on wcep write short summaries about news events and provide a small number of links to relevant source articles. we extract the summaries and source articles from wcep and increase the number of source articles per summary by searching for similar articles in the common crawl news dataset foot_1 . as a result, we obtain large clusters of highly redundant news articles, resembling the output of news clustering applications. we summarize our contributions as follows: ## dataset construction wikipedia current events portal: wcep lists current news events on a daily basis. each news event is presented as a summary with at least one link to external news articles. according to the editing guidelines foot_2 , the summaries must be short, up to 30-40 words, and written in complete sentences in the present tense, avoiding opinions and sensationalism. each event must be of international interest. summaries are written in english, and news sources are preferably english. ## conclusion we present a new large-scale mds dataset for the news domain, consisting of large clusters of news articles, associated with short summaries about news events. we hope this dataset will facilitate the creation of real-world mds systems for use cases such as summarizing news clusters or search results. we conducted extensive experiments to establish baseline results, and we hope that future work on mds will use this dataset as a benchmark. important challenges for future work include how to scale deep learning methods to such large amounts of source documents and how to close the gap to the oracle methods.