Fetching the paper…
Reading the bibliography…
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers.
Hierarchical transformers for multi-document summarization
Yang Liu and Mirella Lapata. 2019a · 1905
Earlier work this paper cites.
Finite geometrical systems
Friedrich Wilhelm Levi. 1942 · 1942
Earlier work this paper cites.
Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2004
Earlier work this paper cites.
Using citations to generate surveys of scientific paradigms
Saif Mohammad, Bonnie Dorr, Melissa Egan, Ahmed Hassan, Pradeep Muthukrishnan, Vahed Qazvinian, Dragomir Radev, and David Zajic. 2009 · 2009
Earlier work this paper cites.
Towards automated related work summarization
Cong Duy Vu Hoang and Min-Yen Kan. 2010 · 2010
Earlier work this paper cites.
Automatic keyword extraction from individual documents
Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010 · 2010
Earlier work this paper cites.
Automatic generation of related work sections in scientific papers: an optimization approach
Yue Hu and Xiaojun Wan. 2014 · 2014
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
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
Cited alongside, same era.
Deliberation networks: Sequence generation beyond one-pass decoding
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, and Tie-Yan Liu. 2017 · 2017
Cited alongside, same era.
Keyphraseds: Automatic generation of survey by exploiting keyphrase information
Shansong Yang, Weiming Lu, Dezhi Yang, Xi Li, Chao Wu, and Baogang Wei. 2017 · 2017
Cited alongside, same era.
Neural related work summarization with a joint context-driven attention mechanism
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019b · 2019
Later among the works it cites.
Entity, relation, and event extraction with contextualized span representations
David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 2019
Later among the works it cites.
Multi-granularity interaction network for extractive and abstractive multi-document summarization
Hanqi Jin, Tianming Wang, and Xiaojun Wan. 2020 · 2020
Later among the works it cites.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
Leveraging graph to improve abstractive multi-document summarization
Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, and Junping Du. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yongzhen Wang, Xiaozhong Liu, and Zheng Gao. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Richard Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
Cited alongside, same era.
Hsds: An abstractive model for automatic survey generation
Xiao-Jian Jiang, Xian-Ling Mao, Bo-Si Feng, Xiaochi Wei, Bin-Bin Bian, and Heyan Huang. 2019 · 2019
Cited alongside, same era.
Text generation from knowledge graphs with graph transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
Incremental transformer with deliberation decoder for document grounded conversations
Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, and Jie Zhou. 2019 · 2019
Cited alongside, same era.
Surveyor: A system for generating coherent survey articles for scientific topics
Rahul Jha, Reed Coke, and Dragomir Radev. 2015a
Cited in the paper.
Multi-xscience: A large-scale dataset for extreme multi-document summarization of scientific articles
Yao Lu, Yue Dong, and Laurent Charlin. 2020 · 2020
Later among the works it cites.
Heterogeneous graph neural networks for extractive document summarization
Danqing Wang, Pengfei Liu, Yining Zheng, Xipeng Qiu, and Xuan-Jing Huang. 2020 · 2020
Later among the works it cites.
Capturing relations between scientific papers: An abstractive model for related work section generation
Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, and Rui Yan. 2021 · 2021
Later among the works it cites.
Explaining relationships between scientific documents
Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A Smith. 2021 · 2021
Later among the works it cites.
Darsh J Shah and Regina Barzilay. 2021 · 2021
Later among the works it cites.