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Interactive Query-Assisted Summarization via Deep Reinforcement Learning.

, , , , and . NAACL-HLT, page 2551-2568. Association for Computational Linguistics, (2022)

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Ontology Assisted Crowd Mining., , , , and . Proc. VLDB Endow., 7 (13): 1597-1600 (2014)PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World., and . CIKM, page 2443-2446. ACM, (2017)Automated Selection of Multiple Datasets for Extension by Integration., and . CIKM, page 27-36. ACM, (2021)Interactive Abstractive Summarization for Event News Tweets., , , , , and . EMNLP (System Demonstrations), page 109-114. Association for Computational Linguistics, (2017)Worst-case analysis for interactive evaluation of Boolean provenance., and . TaPP, page 6:1-6:8. ACM, (2022)Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)., , , , and . SIGMOD Conference Companion, page 303-304. ACM, (2023)Extending Multi-Document Summarization Evaluation to the Interactive Setting., , , , , and . NAACL-HLT, page 657-677. Association for Computational Linguistics, (2021)aiDM'22: Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management., , , , and . SIGMOD Conference, page 2550-2551. ACM, (2022)On Provenance Minimization., , , and . ACM Trans. Database Syst., 37 (4): 30:1-30:36 (2012)On the Limitations of Provenance for Queries with Difference., , and . TaPP, USENIX Association, (2011)