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Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization.

, , , , and . EACL (Findings), page 2044-2060. Association for Computational Linguistics, (2023)

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Analyzing the Abstractiveness-Factuality Tradeoff With Nonlinear Abstractiveness Constraints., , , , and . CoRR, (2021)Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval., , , and . SIGIR, page 533-542. ACM, (2015)Integrating extra knowledge into word embedding models for biomedical NLP tasks., , , , , and . IJCNN, page 968-975. IEEE, (2017)A New TV World for Kids - When ZUI Meets Deep Learning., , , , , and . MIPR, page 124-129. IEEE, (2018)Mobile App Retrieval for Social Media Users via Inference of Implicit Intent in Social Media Text., , , and . CIKM, page 959-968. ACM, (2016)Transductive Learning for Abstractive News Summarization., , , , and . CoRR, (2021)Multi-Task Networks with Universe, Group, and Task Feature Learning., , and . ACL (1), page 820-830. Association for Computational Linguistics, (2019)Product review summarization through question retrieval and diversification., , , , and . Inf. Retr. J., 20 (6): 575-605 (2017)Faithfulness-Aware Decoding Strategies for Abstractive Summarization., , , , and . EACL, page 2856-2872. Association for Computational Linguistics, (2023)Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters., , , , and . NAACL-HLT, page 4768-4779. Association for Computational Linguistics, (2021)