A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1302–1308 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1302 A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal Demian Gholipour Ghalandari1,2, Chris Hokamp1, Nghia The Pham1, John Glover1, Georgiana Ifrim2 1Aylien Ltd., Dublin, Ireland 2Insight Centre for Data Analytics, University College Dublin, Ireland 1{first-name}@aylien.com georgiana.ifrim@insight-centre.org 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 Cur- rent 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. The dataset is available at https://github.com/complementizer/ wcep-mds-dataset. 1 Introduction Text summarization has recently received increased attention with the rise of deep learning-based end- to-end models, both for extractive and abstractive variants. However, so far, only single-document summarization has profited from this trend. Multi- document 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 (Vaswani et al., 2017; Paulus et al., 2018; Devlin et al., 2019), and makes a robust evaluation difficult. Recently, sev- eral larger MDS datasets have been created: Zopf (2018); Liu et al. (2018); Fabbri et al. (2019). How- ever, 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) • Defining the Heisei Era: Just how peaceful were the past 30 years? • As a New Emperor ls Enthroned in Japan, His Wife Won’t Be Al- lowed to Watch Sample Headlines from Common Crawl • Japanese Emperor Akihito to abdicate after three decades on throne • Japan’s Emperor Akihito says he is abdicating as of Tuesday at a ceremony, in his final official address to his people • Akihito begins abdication rituals as Japan marks end of era Table 1: Example event summary and linked source ar- ticles from the Wikipedia Current Events Portal, and additional extracted articles from Common Crawl. cases with large automatically aggregated collec- tions of news articles, focused on particular news events. This includes news event detection, news article search, and timeline generation. Given the prevalence of …

Content truncated to 500 words