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Summarizing a Document by Trimming the Discourse Tree.

, , , , , and . IEEE ACM Trans. Audio Speech Lang. Process., 23 (11): 2081-2092 (2015)

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A Machine Learning Approach for QA and Novelty Tracks: NTT System Description., , , and . TREC, volume 500-251 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2002)Unsupervised group matching with application to cross-lingual topic matching without alignment information., , , and . Data Min. Knowl. Discov., 31 (2): 350-370 (2017)Identifying Bloggers' Residential Areas., , , and . AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, page 231-236. AAAI, (2006)SODA: Story Oriented Dense Video Captioning Evaluation Framework., , , , and . ECCV (6), volume 12351 of Lecture Notes in Computer Science, page 517-531. Springer, (2020)Topic Models for Unsupervised Cluster Matching., , and . IEEE Trans. Knowl. Data Eng., 30 (4): 786-795 (2018)Dependency-based Sentence Alignment for Multiple Document Summarization., , , and . COLING, (2004)Text Summarization Model based on Redundancy-Constrained Knapsack Problem., , , and . COLING (Posters), page 893-902. Indian Institute of Technology Bombay, (2012)Dependency-based Discourse Parser for Single-Document Summarization., , , and . EMNLP, page 1834-1839. ACL, (2014)Sentence Compression with Semantic Role Constraints., , , and . ACL (2), page 349-353. The Association for Computer Linguistics, (2012)A Query-Focused Summarization Method that Guarantees the Inclusion of Query Words., , , , and . DEXA Workshops, page 126-130. IEEE Computer Society, (2012)