[1] LIAKATA M, DOBNIK S, Saha S, et al. A discourse-driven content model for summarising scientific articles evaluated in a complex question answering task[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2013: 747-757.
[2] MEYER T,POPESCU BELIS A. Using sense-labeled discourse connectives for statistical machine translation[C]//Proceedings of the EACL Workshop on Hybrid Approaches to Machine Translation, 2012.
[3] GUZMN F, JOTY S, MRQUEZ L, et al. Using discourse structure improves machine translation evaluation[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014: 687-698.
[4] PELDSZUS A, STEDE M. Joint prediction in mst-style discourse parsing for argumentation mining[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2015: 938-948.
[5] ATKINSON J, MUNOZ R. Rhetorics-based multi-document summarization[J]. Expert Systems with Applications, 2013, 40(11): 4346-4352.
[6] FERREIRA R, DE SOUZA CABRAL L, FREITAS F, et al. A multi-document summarization system based on statistics and linguistic treatment[J]. Expert Systems with Applications, 2014, 41(13): 5780-5787.
[7] COHAN A,GOHARIAN N. Scientific article summarization using citation-context and article's discourse structure[J]. arXiv preprint arXiv: 1704.06619, 2017.
[8] PRESUTTI V, DRAICCHIO F, GANGEMI A. Knowledge extraction based on discourse representation theory and linguistic frames[C]//Proceedings of the International Conference on Knowledge Engineering and Knowledge Management. Springer, Berlin, Heidelberg, 2012: 114-129.
[9] ZOU B, ZHOU G, ZHU Q. Negation focus identification with contextual discourse information[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014: 522-530.
[10] JIANG F, XU S, CHU X, et al. MCDTB: A macro-level Chinese discourse treebank[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018: 3493-3504.
[11] FAN Y, JIANG F, CHU X, et al. Combining global and local information to recognize Chinese macro discourse structure [C]//Proceedings of the 19th Chinese National Conference on Computational Linguistics, 2020: 183-194.
[12] LIU L, LIN X,JOTY S, et al. Hierarchical pointer net parsing[J]. arXiv preprint arXiv: 1908.11571, 2019.
[13] LIN X, JOTY S, JWALAPURAM P, et al. A unified linear-time framework for sentence-level discourse parsing[OL]https://www.aclweb.org/anthology/p19-1400.
[14] KOTO F, LAU J H, BALDWIN T. Top-down discourse parsing via sequence labelling[J]. arXiv preprint arXiv: 2102.02080, 2021.
[15] ZHOU Y, CHU X, LI P, et al. Constructing Chinese macro discourse tree via multiple views and word pair similarity[C]//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2019: 773-786.
[16] JIANG F, CHU X, LI P, et al. Chinese paragraph-level discourse parsing with global backward and local reverse reading[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 5749-5759.
[17] JIANG F, FAN Y, CHU X, et al. Hierarchical macro discourse parsing based on topic segmentation[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(14): 13152-13160.
[18] FENG V W, HIRST G. A linear-time bottom-up discourse parser with constraints and post-editing[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014: 511-521.
[19] LI Q, LI T, CHANG B. Discourse parsing with attention-based hierarchical neural networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016: 362-371.
[20] KOBAYASHI N,HIRAO T, NAKAMURA K, et al. Split or merge: Which is better for unsupervised RST parsing?[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 5797-5802.
[21] CARLSON L,MARCU D, OKUROWSKI M E. Building a discourse-tagged corpus in the framework of rhetorical structure theory[M]. Current and New Directions in Discourse and Dialogue. Springer, Dordrecht, 2003: 85-112.
[22] MABONA A, RIMELL L, CLARK S, et al. Neural generative rhetorical structure parsing[J]. arXiv preprint arXiv: 1909.11049, 2019.
[23] FRIED D, STERN M, KLEIN D. Improving neural parsing by disentangling model combination and reranking effects[J].arXiv preprint arXiv: 1707.03058, 2017.
[24] ZHANG L, TAN X, KONG F, et al. A recursive information flow gated model for RST-style text-level discourse parsing[C]//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2019: 231-241.
[25] KOBAYASHI N,HIRAO T, KAMIGAITO H, et al. Top-down RST parsing utilizing granularity levels in documents[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(05): 8099-8106.
[26] NGUYEN T T, NGUYEN X P, JOTY S, et al. RST parsing from scratch[J]. arXiv preprint arXiv: 2105.10861, 2021.
[27] VINYALS O, FORTUNATO M, JAITLY N. Pointer networks[J]. arXiv preprint arXiv: 1506.03134, 2015.
[28] CUI Y, CHE W, LIU T, et al. Revisiting pre-trained models for Chinese natural language processing[J]. arXiv preprint arXiv: 2004.13922, 2020.
[29] CHO K, VANMERRINBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv: 1406.1078, 2014.
[30] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.