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Process."],"published-print":{"date-parts":[[2019,9,30]]},"abstract":"<jats:p>Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantically unrelated words with respect to the source content as the most probable output. The problem can be attributed to heuristically constructed training data, where summaries can be unrelated to the source content, thus containing semantically unrelated words and spurious word correspondence. In this article, we propose a regularization approach for the sequence-to-sequence model and make use of what the model has learned to regularize the learning objective to alleviate the effect of the problem. In addition, we propose a practical human evaluation method to address the problem that the existing automatic evaluation method does not evaluate the semantic consistency with the source content properly. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms almost all the existing models. Especially, the proposed approach improves the semantic consistency by 4% in terms of human evaluation.<\/jats:p>","DOI":"10.1145\/3314934","type":"journal-article","created":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T12:20:39Z","timestamp":1556713239000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency"],"prefix":"10.1145","volume":"18","author":[{"given":"Bingzhen","family":"Wei","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6994-2114","authenticated-orcid":false,"given":"Xuancheng","family":"Ren","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Cai","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shannxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Su","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Sun","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,4,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CYBConf.2017.7985811"},{"key":"e_1_2_1_2_1","volume-title":"Neural machine translation by jointly learning to align and translate. 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Faithful to the original: Fact aware neural abstractive summarization. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, California, 4784--4791. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16121"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press\/International Joint Conferences on Artificial Intelligence","author":"Chen Qian","year":"2016","unstructured":"Qian Chen , Xiaodan Zhu , Zhenhua Ling , Si Wei , and Hui Jiang . 2016 . Distraction-based neural networks for modeling documents . In Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press\/International Joint Conferences on Artificial Intelligence , Palo Alto, California, 2754--2760. http:\/\/www.ijcai.org\/Abstract\/16\/391 Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. 2016. Distraction-based neural networks for modeling documents. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press\/International Joint Conferences on Artificial Intelligence, Palo Alto, California, 2754--2760. http:\/\/www.ijcai.org\/Abstract\/16\/391"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. The Association for Computational Linguistics","author":"Chopra Sumit","unstructured":"Sumit Chopra , Michael Auli , and Alexander M. Rush . 2016. Abstractive sentence summarization with attentive recurrent neural networks . In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. The Association for Computational Linguistics , Stroudsburg, Pennsylvania, 93--98. Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016. Abstractive sentence summarization with attentive recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. The Association for Computational Linguistics, Stroudsburg, Pennsylvania, 93--98."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"e_1_2_1_7_1","volume-title":"Long Papers","author":"Gu Jiatao","year":"2016","unstructured":"Jiatao Gu , Zhengdong Lu , Hang Li , and Victor O. K . Li . 2016 . Incorporating copying mechanism in sequence-to-sequence learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics , Volume 1 : Long Papers . The Association for Computer Linguistics , Stroudsburg, PA , 1631--1640. Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. 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