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The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach.

, , and . COLING, page 1535-1545. ACL, (2016)

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Estimating Summary Quality with Pairwise Preferences.. NAACL-HLT, page 1687-1696. Association for Computational Linguistics, (2018)Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization., , and . CoNLL, page 84-94. ACL, (2016)The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach., , and . COLING, page 1535-1545. ACL, (2016)Improving Answer Selection with Analogy-Preserving Sentence Embeddings., , and . LWDA, volume 2454 of CEUR Workshop Proceedings, page 84-88. CEUR-WS.org, (2019)SeqCluSum: Combining Sequential Clustering and Contextual Importance Measuring to Summarize Developing Events over Time.. TREC, Special Publication 500-319, National Institute of Standards and Technology (NIST), (2015)Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization., , and . COLING, page 1071-1082. ACL, (2016)Which Scores to Predict in Sentence Regression for Text Summarization?, , and . NAACL-HLT, page 1782-1791. Association for Computational Linguistics, (2018)Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus.. LREC, European Language Resources Association (ELRA), (2018)What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization., , , , , , , , , and . SNAMS, page 272-277. IEEE, (2018)