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Inf. Syst."],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>\n            Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user\u2019s preference) remains unexplored. In this article, we present\n            <jats:bold>CATS,<\/jats:bold>\n            an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN\/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings\u2019 transcripts datasets, AMI and\n            <jats:bold>International Computer Science Institute(ICSI)<\/jats:bold>\n            , results in merely a few hundred training documents.\n          <\/jats:p>","DOI":"10.1145\/3464299","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T21:29:33Z","timestamp":1635197373000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["CATS: Customizable Abstractive Topic-based Summarization"],"prefix":"10.1145","volume":"40","author":[{"given":"Seyed Ali","family":"Bahrainian","sequence":"first","affiliation":[{"name":"AI Lab, Brown University, Waterman St., Providence, RI"}]},{"given":"George","family":"Zerveas","sequence":"additional","affiliation":[{"name":"AI Lab, Brown University, Waterman St., Providence, RI"}]},{"given":"Fabio","family":"Crestani","sequence":"additional","affiliation":[{"name":"Informatics Department, University of Lugano, Lugano, Switzerland"}]},{"given":"Carsten","family":"Eickhoff","sequence":"additional","affiliation":[{"name":"AI Lab, Brown University, Waterman St., Providence, RI"}]}],"member":"320","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3295750.3298923"},{"key":"e_1_2_1_2_1","volume-title":"3rd International Conference on Learning Representations, ICLR Conference Track Proceedings.","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural Machine Translation by Jointly Learning to Align and Translate . In 3rd International Conference on Learning Representations, ICLR Conference Track Proceedings. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. 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Rush . 2018. Bottom-up abstractive summarization . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.4098\u20134109 . Sebastian Gehrmann, Yuntian Deng, and Alexander M. Rush. 2018. Bottom-up abstractive summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.4098\u20134109."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"e_1_2_1_18_1","volume-title":"Griffiths and Mark Steyvers","author":"Thomas","year":"2004","unstructured":"Thomas L. Griffiths and Mark Steyvers . 2004 . Finding scientific topics. In Proceedings of the National Academy of Sciences . 5228\u20135235. Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. In Proceedings of the National Academy of Sciences. 5228\u20135235."},{"key":"e_1_2_1_19_1","unstructured":"Jiatao Gu Zhengdong Lu Hang Li and Victor OK Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. arXiv:1603.06393. Retrieved from https:\/\/arxiv.org\/abs\/1603.06393.  Jiatao Gu Zhengdong Lu Hang Li and Victor OK Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. arXiv:1603.06393. Retrieved from https:\/\/arxiv.org\/abs\/1603.06393."},{"key":"e_1_2_1_20_1","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv:1606.08415. Retrieved from https:\/\/arxiv.org\/abs\/1606.08415.  Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv:1606.08415. 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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461. Retrieved from https:\/\/arxiv.org\/abs\/1910.13461."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1441"},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the Workshop on Text Summarization Branches Out.","author":"Lin Chin-Yew","year":"2004","unstructured":"Chin-Yew Lin . 2004 . Rouge: A package for automatic evaluation of summaries . In Proceedings of the Workshop on Text Summarization Branches Out. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Proceedings of the Workshop on Text Summarization Branches Out."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298681"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K16-1028"},{"key":"e_1_2_1_29_1","unstructured":"Romain Paulus Caiming Xiong and Richard Socher. 2017. 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