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Writing a survey paper on one research topic usually needs to cover the salient content from numerous related papers, which can be modeled as a multi-document summarization (MDS) task.
Measuring nominal scale agreement among many raters
Joseph L Fleiss · 1971
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Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev · 2004
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Current findings from research on structured abstracts
James Hartley · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau · 2004
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Natural language processing with Python
Steven Bird, Ewan Klein, and Edward Loper · 2009
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Current findings from research on structured abstracts: An update
James Hartley · 2014
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An overview of microsoft academic service (mas) and applications
Arnab Sinha, Zhihong Shen, Yang Song, et al · 2015
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Attention is all you need
Ashish Vaswani et al · 2017
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Construction of the literature graph in semantic scholar
Waleed Ammar, Dirk Groeneveld, et al · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi · 2018
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Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, et al · 2018
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Pretrained language models for sequential sentence classification
Arman Cohan, Iz Beltagy, King, et al · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin et al · 2019
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Richard Fabbri, Irene Li, Tianwei She, et al · 2019
Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, et al · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, et al · 2020
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Multi-XScience: A large-scale dataset for extreme multi-document summarization of scientific articles
Yao Lu, Yue Dong, and Laurent Charlin · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, et al · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, et al · 2020
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Structured summarization of academic publications
Alexios Gidiotis and Grigorios Tsoumakas · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, et al · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott et al · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang et al · 2020
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Publication patterns’ changes due to the covid-19 pandemic: A longitudinal and short-term scientometric analysis
Shir Aviv-Reuven and Ariel Rosenfeld · 2021
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Highlight-transformer: Leveraging key phrase aware attention to improve abstractive multi-document summarization
Shuaiqi Liu, Jiannong Cao, et al · 2021
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Bringing structure into summaries: a faceted summarization dataset for long scientific documents
Rui Meng, Khushboo Thaker, Lei Zhang, et al · 2021
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Key phrase aware transformer for abstractive summarization
Shuaiqi Liu, Jiannong Cao, et al · 2022
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