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Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction., , and . ACL (1), page 1388-1398. Association for Computational Linguistics, (2019)Probing Linguistic Features of Sentence-Level Representations in Relation Extraction., , and . ACL, page 1534-1545. Association for Computational Linguistics, (2020)A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries., , , , and . QoMEX, page 1-3. IEEE, (2019)Improving Relation Extraction by Pre-trained Language Representations., , and . AKBC, (2019)Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs., , and . WASSA@EMNLP, page 316-321. Association for Computational Linguistics, (2018)MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset., , and . ACL (1), page 3785-3801. Association for Computational Linguistics, (2023)<i>Verbundprojekt: Smart Data Web : zusammenfassender Abschlussbericht : Vorhaben: Verbundprojekt SDW Smart Data Web</i>, , , , , , , , , and 9 other author(s). Kaiserslautern, (05 submitted)A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products., , , and . CoRR, (2020)Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction., , and . CoRR, (2020)Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling., , , , , and . LREC, page 6680-6689. European Language Resources Association, (2020)