{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:53:37Z","timestamp":1762102417461},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Sentence ordering is to restore the original paragraph from a set of sentences. It involves capturing global dependencies among sentences regardless of their input order. In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \\citep{Zhang:acl18} to accurately learn semantic representations of the sentences. Instead of assuming connections between all pairs of input sentences, we use entities that are shared among multiple sentences to make more expressive graph representations with less noise. Experimental results show that our proposed model outperforms the existing state-of-the-art systems on several benchmark datasets, demonstrating the effectiveness of our model. We also conduct a thorough analysis on how entities help the performance. Our code is available at https:\/\/github.com\/DeepLearnXMU\/NSEG.git.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/748","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5387-5393","source":"Crossref","is-referenced-by-count":19,"title":["Graph-based Neural Sentence Ordering"],"prefix":"10.24963","author":[{"given":"Yongjing","family":"Yin","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"given":"Linfeng","family":"Song","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, NY, U.S."},{"name":"Tencent AI Lab, Bellevue, WA, U.S."}]},{"given":"Jinsong","family":"Su","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"given":"Jiali","family":"Zeng","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"given":"Chulun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, NY, U.S."}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:33Z","timestamp":1564285893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/748"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/748","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}