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InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT., , , , , , и . EMNLP (Findings), стр. 13879-13892. Association for Computational Linguistics, (2023)Identifying content for planned events across social media sites., , , и . WSDM, стр. 533-542. ACM, (2012)Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference., , и . EMNLP (1), стр. 1406-1417. Association for Computational Linguistics, (2021)Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models., , , и . ACL, стр. 4859-4870. Association for Computational Linguistics, (2020)Generate rather than Retrieve: Large Language Models are Strong Context Generators., , , , , , , , и . ICLR, OpenReview.net, (2023)LMGQS: A Large-scale Dataset for Query-focused Summarization., , , , , , , , и . EMNLP (Findings), стр. 14764-14776. Association for Computational Linguistics, (2023)How Does In-Context Learning Help Prompt Tuning?, , , , и . EACL (Findings), стр. 156-165. Association for Computational Linguistics, (2024)Automatic Identification and Presentation of Twitter Content for Planned Events., , , , и . ICWSM, The AAAI Press, (2011)The Trade-offs of Domain Adaptation for Neural Language Models., и . ACL (1), стр. 3802-3813. Association for Computational Linguistics, (2022)In-Context Demonstration Selection with Cross Entropy Difference., , , , , , и . EMNLP (Findings), стр. 1150-1162. Association for Computational Linguistics, (2023)