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Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowledge for contextualising its background and main contributions.
Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset
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Latent dirichlet allocation
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Jensen’s operator inequality
Frank Hansen and Gert K Pedersen. 2003 · 2003
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Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 human language technology conference of the North American chapter of the association for computational linguistics . 150–157
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
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Lexrank: Graph-based lexical centrality as salience in text summarization
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Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing . 404–411
Rada Mihalcea and Paul Tarau. 2004 · 2004
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Scientific Paper Summarization Using Citation Summary Networks. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008) . 689–696
Vahed Qazvinian and Dragomir Radev. 2008 · 2008
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Coherent citation-based summarization of scientific papers. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies . 500–509
Amjad Abu-Jbara and Dragomir Radev. 2011 · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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The Number of Scholarly Documents on the Public Web
Madian Khabsa and C. Lee Giles. 2014 · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Scientific Article Summarization Using Citation-Context and Article’s Discourse Structure. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . 390–400
Arman Cohan and Nazli Goharian. 2015 · 2015
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Variational Graph Auto-Encoders
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Thirty-First AAAI Conference on Artificial Intelligence
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1073–1083
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Representations (ICLR 2017)
Max Welling and Thomas N Kipf. 2016 · 2017
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021 · 2021
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Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021 . 1229–1243
Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, and Xiaojie Wang. 2021 · 2021
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SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) . 1065–1072
Yixin Liu and Pengfei Liu. 2021 · 2021
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Improving Factual Consistency of Abstractive Summarization via Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 6881–6894
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Cited alongside, same era.
Contextual Web Summarization: A Supervised Ranking Approach. In Companion Proceedings of the The Web Conference 2018 (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 105–106
Amit Sarkar and G. Srinivasaraghavan. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT . 4171–4186
Jacob Devlin Kenton, Ming-Wei Chang, Toutanova, and Lee Kristina. 2019 · 2019
Cited alongside, same era.
Text Summarization with Pretrained Encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3730–3740
Yang Liu and Mirella Lapata. 2019 · 2019
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Scisummnet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 7386–7393
Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R Fabbri, Irene Li, Dan Friedman, and Dragomir R Radev. 2019 · 2019
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 7871–7880
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Cited text span identification for scientific summarisation using pre-trained encoders
Chrysoula Zerva, Minh-Quoc Nghiem, Nhung TH Nguyen, and Sophia Ananiadou. 2020 · 2020
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Enhancing scientific papers summarization with citation graph. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 12498–12506
Chenxin An, Ming Zhong, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2021 · 2021
Cited alongside, same era.
Feng Nan, Cicero dos Santos, Henghui Zhu, Patrick Ng, Kathleen Mckeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O Arnold, and Bing Xiang. 2021 · 2021
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Pre-trained language models in biomedical domain: A systematic survey
Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, et al · 2021
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Contrastive Aligned Joint Learning for Multilingual Summarization. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . 2739–2750
Danqing Wang, Jiaze Chen, Hao Zhou, Xipeng Qiu, and Lei Li. 2021a · 2021
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Graph Relational Topic Model with Higher-order Graph Attention Auto-encoders. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . 2604–2613
Qianqian Xie, Jimin Huang, Pan Du, and Min Peng. 2021a · 2021
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Graph neural collaborative topic model for citation recommendation
Qianqian Xie, Yutao Zhu, Jimin Huang, Pan Du, and Jian-Yun Nie. 2021b · 2021
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Graph Enhanced Contrastive Learning for Radiology Findings Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 4677–4688
Jinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li, Xiang Wan, and Tsung-Hui Chang. 2022 · 2022
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Sequence-Based Extractive Summarisation for Scientific Articles. In Companion Proceedings of the Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22) . Association for Computing Machinery, New York, NY, USA, 751–757
Daniel Kershaw and Rob Koeling. 2022 · 2022
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Readability Controllable Biomedical Document Summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2022 · 2022
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Pre-trained language models with domain knowledge for biomedical extractive summarization
Qianqian Xie, Jennifer Amy Bishop, Prayag Tiwari, and Sophia Ananiadou. 2022a · 2022
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