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Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n            Today, timestamped web documents related to a general news query flood the Internet, and timeline summarization targets this concisely by summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this article we propose our Unified Timeline Summarizer, which can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information retained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting a summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that our Unified Timeline Summarizer achieves state-of-the-art performance in terms of both automatic and human evaluations.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3517221","type":"journal-article","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T09:04:34Z","timestamp":1646471074000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Follow the Timeline! Generating an Abstractive and Extractive Timeline Summary in Chronological Order"],"prefix":"10.1145","volume":"41","author":[{"given":"Xiuying","family":"Chen","sequence":"first","affiliation":[{"name":"King Abdullah University of Science and Technology"}]},{"given":"Mingzhe","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Shen","family":"Gao","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Zhangming","family":"Chan","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology"}]},{"given":"Xiangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Notre Dame; King Abdullah University of Science and Technology"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"University of China"}]}],"member":"320","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of OSDI","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, et\u00a0al. 2016. 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