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In this work, we propose a novel training framework based on Constrained Markov Decision Process (CMDP), which conveniently includes a reward function along with a set of constraints, to facilitate better summarization control. The reward function encourages the generation to resemble the human-written reference, while the constraints are used to explicitly prevent the generated summaries from violating user-imposed requirements. Our framework can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness, as we devise specific constraints for each of these aspects. Extensive experiments on popular benchmarks show that our CMDP framework helps generate informative summaries while complying with a given attribute\u2019s requirement.1<\/jats:p>","DOI":"10.1162\/tacl_a_00423","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T14:32:19Z","timestamp":1637332339000},"page":"1213-1232","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":11,"title":["Controllable Summarization with Constrained Markov Decision Process"],"prefix":"10.1162","volume":"9","author":[{"given":"Hou Pong","family":"Chan","sequence":"first","affiliation":[{"name":"University of Macau, Macau SAR, China. hpchan@um.edu.mo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, USA. 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