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Predicting Summary Quality using Limited Human Input., and . TAC, NIST, (2009)Exploring Substitutability through Discourse Adverbials and Multiple Judgments., , , , and . IWCS(1), The Association for Computer Linguistics, (2017)Source-summary Entity Aggregation in Abstractive Summarization., , and . COLING, page 6019-6034. International Committee on Computational Linguistics, (2022)Can You Summarize This? Identifying Correlates of Input Difficulty for Multi-Document Summarization., and . ACL, page 825-833. The Association for Computer Linguistics, (2008)LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction., , , , , and . ACL (1), page 10251-10269. Association for Computational Linguistics, (2023)Automatic sense prediction for implicit discourse relations in text., , and . ACL/IJCNLP, page 683-691. The Association for Computer Linguistics, (2009)Automatically Evaluating Content Selection in Summarization without Human Models., and . EMNLP, page 306-314. ACL, (2009)Recovering discourse relations: Varying influence of discourse adverbials., , , , and . LSDSem@EMNLP, page 22-31. Association for Computational Linguistics, (2015)Natural Language Processing for Social Media.. Computational Linguistics, 42 (4): 833-836 (2016)Automatically Assessing Machine Summary Content Without a Gold Standard., and . Computational Linguistics, 39 (2): 267-300 (2013)