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

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Title: A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Authors: Ghalandari, Demian GholipourHokamp, ChrisPham, Nghia TheGlover, JohnIfrim, Georgiana
Permanent link: http://hdl.handle.net/10197/12036
Date: 10-Jul-2020
Online since: 2021-03-11T16:05:39Z
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.
Funding Details: Irish Research Council
Science Foundation Ireland
Funding Details: Aylien Ltd.
Type of material: Conference Publication
Keywords: Multi-document summarizationNews eventsDeep learning methods
DOI: 10.18653/v1/2020.acl-main.120
Other versions: https://acl2020.org/
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Conference Details: The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Online, 5-10 July 2020
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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