{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:20:05Z","timestamp":1753600805091,"version":"3.41.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61673290, 61876217, 61672371, and 61876121"],"award-info":[{"award-number":["61673290, 61876217, 61672371, and 61876121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science 8 Technology Development Project of Suzhou","award":["SYG201817"],"award-info":[{"award-number":["SYG201817"]}]},{"DOI":"10.13039\/501100005065","name":"Innovative Team of Jiangsu Province","doi-asserted-by":"crossref","award":["XYDXX-086"],"award-info":[{"award-number":["XYDXX-086"]}],"id":[{"id":"10.13039\/501100005065","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2020,11,30]]},"abstract":"<jats:p>Text summarization is one of the significant tasks of natural language processing, which automatically converts text into a summary. Some summarization systems, for short\/long English, and short Chinese text, benefit from advances in the neural encoder-decoder model because of the availability of large datasets. However, the long Chinese text summarization research has been limited to datasets of a couple of hundred instances. This article aims to explore the long Chinese text summarization task. To begin with, we construct a first large-scale, long Chinese text summarization corpus, the Long Chinese Summarization of Police Inquiry Record Text (LCSPIRT). Based on this corpus, we propose a sequence-to-sequence (Seq2Seq) model that incorporates a global encoding process with an attention mechanism. 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