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Automated scholarly paper review: Concepts, technologies, and challenges

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Lin, Jialiang, Song, Jiaxin, Zhou, Zhangping, Chen, Yidong, Shi, Xiaodong·2023·Information Fusion
DOI 10.1016/j.inffus.2023.101830
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  1. (2016) An interesting year. Nature Microbiology 2(1):16,259. https://doi.org/10.1038/ nmicrobiol.2016.259
  2. Abdi A, Idris N, Alguliyev RM, et al (2015) PDLK: Plagiarism detection using linguistic knowledge. Expert Systems with Applications 42(22):8936-8946. https://doi.org/10. 1016/J.ESWA.2015.07.048
  3. Abdi A, Shamsuddin SM, Idris N, et al (2017) A linguistic treatment for automatic external plagiarism detection. Knowledge-Based Systems 135:135-146. https://doi.org/10.1016/j.knosys. 2017.08.008 · DOI
  4. Adhikari A, Ram A, Tang R, et al (2019) DocBERT: BERT for document classification. arXiv preprint arXiv:1904.08398 · arXiv
  5. Agarwal S, Yu H (2009) Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion. Bioinformatics 25(23):3174-3180. https:// doi.org/10.1093/bioinformatics/btp548 · DOI
  6. Ahmad F, Kondrak G (2005) Learning a spelling error model from search query logs. In: HLTEMNLP, https://doi.org/10.3115/1220575. 1220695 · DOI
  7. Ahuja L, Gupta V, Kumar R (2020) A new hybrid technique for detection of plagiarism from text documents. Arabian Journal for Science and Engineering 45(12):9939-9952. https://doi.org/ 10.1007/s13369-020-04565-9 · DOI
  8. Ali Z, Kefalas P, Muhammad K, et al (2020) Deep learning in citation recommendation models survey. Expert Systems with Applications 162:113,790. https://doi.org/10.1016/j. eswa.2020.113790 · DOI
  9. Amancio DR (2015) Comparing the topological properties of real and artificially generated scientific manuscripts. Scientometrics 105(3):1763-1779. https://doi.org/10.1007/ s11192-015-1637-z
  10. Amplayo RK, Hong S, Song M (2018) Networkbased approach to detect novelty of scholarly literature. Information Sciences 422:542-557. https://doi.org/10.1016/j.ins.2017.09.037 · DOI
  11. Anderson J, Stajano F, Watson RNM (2011) How to keep bad papers out of conferences (with minimum reviewer effort). In: Security Protocols XIX, https://doi.org/10.1007/ 978-3-642-25867-1 34
  12. Anjum O, Gong H, Bhat S, et al (2019) PaRe: A paper-reviewer matching approach using a common topic space. In: EMNLP-IJCNLP, https: //doi.org/10.18653/v1/D19-1049 · DOI
  13. Antoniou G, Batsakis S, Mutharaju R, et al (2018) A survey of large-scale reasoning on the Web of data. The Knowledge Engineering Review 33:e21. https://doi.org/10.1017/ S0269888918000255
  14. Association AP (2020) Publication manual of the American Psychological Association, seventh edn. American Psychological Association, https://doi.org/10.1037/0000165-000 · DOI
  15. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: ICLR
  16. Bao P, Hong W, Li X (2021) Predicting paper acceptance via interpretable decision sets. In: WWW, https://doi.org/10.1145/3442442. 3451370 · DOI
  17. Baroni M (2019) Linguistic generalization and compositionality in modern artificial neural networks. Philosophical Transactions of the Royal Society B 375(1791):20190,307. https:// doi.org/10.1098/rstb.2019.0307 · DOI
  18. Bartoli A, De Lorenzo A, Medvet E, et al (2016) Your paper has been accepted, rejected, or whatever: Automatic generation of scientific paper reviews. In: CD-ARES, https://doi.org/ 10.1007/978-3-319-45507-5 2 · DOI
  19. Bell S, Yannakoudakis H, Rei M (2019) Context is key: Grammatical error detection with contextual word representations. In: BEA, https: //doi.org/10.18653/v1/W19-4410 · DOI
  20. Breiman L (2001) Random forests. Machine Learning 45(1):5-32. https://doi.org/10.1023/ A:1010933404324
  21. Brezis ES, Birukou A (2020) Arbitrariness in the peer review process. Scientometrics 123(1):393-411. https://doi.org/10.1007/ s11192-020-03348-1
  22. Brody T, Harnad S, Carr L (2006) Earlier web usage statistics as predictors of later citation impact. Journal of the American Society for Information Science and Technology 57(8):1060-1072. https://doi.org/10.1002/ asi.20373
  23. Brown N, Sandholm T (2019) Superhuman AI for multiplayer poker. Science 365(6456):885-890. https://doi.org/10.1126/science.aay2400 · DOI · Paper page
  24. Cabanac G, Labb´ e C (2021) Prevalence of nonsensical algorithmically generated papers in the scientific literature. Journal of the Association for Information Science and Technology 72(12):1461-1476. https://doi.org/10.1002/asi. 24495 · DOI
  25. Checco A, Bracciale L, Loreti P, et al (2021) AI-assisted peer review. Humanities and Social Sciences Communications 8(1):25. https://doi. org/10.1057/s41599-020-00703-8 · DOI · Paper page
  26. Chodorow M, Tetreault J, Han NR (2007) Detection of grammatical errors involving prepositions. In: SIGSEM, https://doi.org/10.3115/ 1654629.1654635
  27. Collins M, Duffy N (2002) New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: ACL, https://doi.org/10.3115/1073083.1073128 · DOI
  28. Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273-297. https:
  29. Dahlin KB, Behrens DM (2005) When is an invention really radical? Defining and measuring technological radicalness. Research Policy 34(5):717-737. https://doi.org/10.1016/j. respol.2005.03.009 · DOI
  30. Daudaravicius V (2015) Automated evaluation of scientific writing: AESW Shared Task proposal. In: BEA, https://doi.org/10.3115/v1/ W15-0607 · DOI
  31. Daudaravicius V, Banchs RE, Volodina E, et al (2016) A report on the Automatic Evaluation of Scientific Writing Shared Task. In: BEA, https: //doi.org/10.18653/v1/W16-0506 · DOI
  32. Daudaraviˇ cius V (2014) Language Editing Dataset of Academic Texts. In: LREC
  33. Deerwester S, Dumais ST, Furnas GW, et al (1990) Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6):391-407. https: //doi.org/10.1002/(SICI)1097-4571(199009)41: 6 ⟨ 391::AID-ASI1 ⟩ 3.0.CO;2-9 · DOI
  34. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39(1):122. https://doi.org/10.1111/j.2517-6161.1977. tb01600.x · DOI
  35. Dong H, Cheng Z, He X, et al (2022) Table pretraining: A survey on model architectures, pretraining objectives, and downstream tasks. In: IJCAI, https://doi.org/10.24963/ijcai.2022/761 · DOI
  36. Dongen Tv, Wenniger GMdB, Schomaker L (2020) SChuBERT: Scholarly document chunks with BERT-encoding boost citation count prediction. In: SDP, https://doi.org/10.18653/v1/ 2020.sdp-1.17 · DOI
  37. Dosovitskiy A, Beyer L, Kolesnikov A, et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR
  38. Ehsan N, Shakery A (2016) Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information. Information Processing & Management 52(6):1004-1017. https://doi.org/10.1016/j.ipm.2016.04.006 · DOI
  39. Eisa TAE, Salim N, Abdelmaboud A (2019) Content-based scientific figure plagiarism detection using semantic mapping. In: IRICT, https: //doi.org/10.1007/978-3-030-33582-3 40 · DOI
  40. Eisa TAE, Salim N, Alzahrani S (2020) Text-based analysis to detect figure plagiarism. In: IRICT, https://doi.org/10.1007/978-3-030-70713-2 47 · DOI
  41. Ernst NA, Carver JC, Mendez D, et al (2021) Understanding peer review of software engineering papers. Empirical Software Engineering 26(5):103. https://doi.org/10. 1007/s10664-021-10005-5
  42. Felice RD, Pulman S (2013) Automatic detection of preposition errors in learner writing. The Calico Journal 26(3):512-528. https://doi.org/10. 1558/CJ.V26I3.512-528
  43. Flor M, Fried M, Rozovskaya A (2019) A benchmark corpus of English misspellings and a minimally-supervised model for spelling correction. In: BEA, https://doi.org/10.18653/v1/ W19-4407 · DOI
  44. Foltz PW, Laham D, Landauer TK (1999) The Intelligent Essay Assessor: Applications to educational technology. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning 1(2):939-944
  45. Froese FJ, Bader K (2019) Surviving the deskreview. Asian Business & Management 18(1):15. https://doi.org/10.1057/s41291-019-00060-8 · DOI
  46. Fu LD, Aliferis CF (2008) Models for predicting and explaining citation count of biomedical articles. In: American Medical Informatics Association Annual Symposium
  47. Gentile AL, Ristoski P, Eckel S, et al (2017) Entity matching on web tables: A table embeddings approach for blocking. In: EDBT, https://doi. org/10.5441/002/edbt.2017.57 · DOI
  48. Gharavi E, Veisi H, Rosso P (2020) Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: No training phase. Neural Computing and Applications 32(14):10,593-10,607. https://doi.org/10.1007/s00521-019-04594-y · DOI
  49. Ghasemi-Gol M, Szekely PA (2018) TabVec: Table vectors for classification of web tables. arXiv preprint arXiv:1802.06290 · arXiv
  50. Ghosal T, Salam A, Tiwari S, et al (2018) TAPDLND 1.0: A corpus for document level novelty detection. In: LREC
  51. Ghosal T, Dey D, Dutta A, et al (2019a) A multiview clustering approach to identify outof-scope submissions in peer review. In: JCDL, https://doi.org/10.1109/jcdl.2019.00086 · DOI
  52. Ghosal T, Sonam R, Ekbal A, et al (2019b) Is the paper within scope? Are you fishing in the right pond? In: JCDL, https://doi.org/10.1109/ JCDL.2019.00040
  53. Ghosal T, Verma R, Ekbal A, et al (2020) An empirical study of importance of different sections in research articles towards ascertaining their appropriateness to a journal. In: ICADL, https://doi.org/10.1007/978-3-030-64452-9 38 · DOI
  54. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5):602-610. https://doi.org/10. 1016/j.neunet.2005.06.042
  55. Gupta Y, Ammanamanchi PS, Bordia S, et al (2021) The effect of pretraining on extractive summarization for scientific documents. In: SDP, https://doi.org/10.18653/v1/2021.sdp-1. 9 · DOI
  56. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: CVPR, https: //doi.org/10.1109/CVPR.2016.90 · DOI · Paper page
  57. Heaven D (2018) AI peer reviewers unleashed to ease publishing grind. Nature 563(7733):609-610. https://doi.org/10.1038/ D41586-018-07245-9
  58. Hingmire S, Chougule S, Palshikar GK, et al (2013) Document classification by topic labeling. In: SIGIR, https://doi.org/10.1145/ 2484028.2484140
  59. Hodge VJ, Austin J (2003) A comparison of standard spell checking algorithms and a novel binary neural approach. IEEE Transactions on Knowledge and Data Engineering 15(5):1073-1081. https://doi.org/10.1109/ TKDE.2003.1232265
  60. Hou Y, Jochim C, Gleize M, et al (2019) Identification of tasks, datasets, evaluation metrics, and numeric scores for scientific leaderboards construction. In: ACL, https://doi.org/10.18653/ v1/P19-1513
  61. Hou Y, Jochim C, Gleize M, et al (2021) TDMSci: A specialized corpus for scientific literature entity tagging of tasks datasets and metrics. In: EACL
  62. Houghton J (2009) Open access: What are the economic benefits? A comparison of the United Kingdom, Netherlands and Denmark. Social Science Research Network https://doi.org/10. 2139/SSRN.1492578
  63. Hu L, Tang Y, Wu X, et al (2021) Considering optimization of English grammar error correction based on neural network. Neural Computing and Applications https://doi.org/10.1007/ s00521-020-05591-2
  64. Huang JB (2018) Deep paper gestalt. arXiv preprint arXiv:1812.08775 · arXiv
  65. Jefferson T, Wager E, Davidoff F (2002) Measuring the quality of editorial peer review. JAMA 287(21):2786-2790. https://doi.org/10. 1001/jama.287.21.2786
  66. Jo H, Kang D, Head A, et al (2021) Modeling mathematical notation semantics in academic papers. In: Findings of EMNLP, https://doi. org/10.18653/v1/2021.findings-emnlp.266 · DOI
  67. Kang D, Ammar W, Dalvi B, et al (2018) A dataset of peer reviews (PeerRead): Collection, insights and NLP applications. In: NAACLHLT, https://doi.org/10.18653/v1/N18-1149 · DOI · Paper page
  68. Kardas M, Czapla P, Stenetorp P, et al (2020) AXCELL: Automatic extraction of results from machine learning papers. In: EMNLP, https:// doi.org/10.18653/v1/2020.emnlp-main.692 https://doi.org/10.18653/v1/2020.emnlp-main.692 · DOI
  69. Ke Z, Ng V (2019) Automated essay scoring: A survey of the state of the art. In: IJCAI, https: //doi.org/10.24963/ijcai.2019/879 · DOI · Paper page
  70. Labb´ e C, Labb´ e D (2013) Duplicate and fake publications in the scientific literature: How many SCIgen papers in computer science? Scientometrics 94(1):379-396. https://doi.org/10. 1007/s11192-012-0781-y
  71. Lai S, Xu L, Liu K, et al (2015) Recurrent convolutional neural networks for text classification. In: AAAI
  72. Laine C (2017) Scientific misconduct hurts. Annals of Internal Medicine 166(2):148-149. https://doi.org/10.7326/M16-2550 · DOI
  73. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: ICML
  74. LeCun Y, Boser B, Denker JS, et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4):541-551. https://doi.org/10.1162/neco.1989.1.4.541 · DOI · Paper page
  75. Lee LH, Lin BL, Yu LC, et al (2016) The NTNUYZU system in the AESW shared task: Automated evaluation of scientific writing using a convolutional neural network. In: BEA, https: //doi.org/10.18653/v1/W16-0513 · DOI
  76. Leng Y, Yu L, Xiong J (2019) DeepReviewer: Collaborative grammar and innovation neural network for automatic paper review. In: ICMI, https://doi.org/10.1145/3340555.3353766 · DOI
  77. Lev G, Shmueli-Scheuer M, Herzig J, et al (2019) TalkSumm: A dataset and scalable annotation method for scientific paper summarization based on conference talks. In: ACL, https://doi. org/10.18653/v1/P19-1204 · DOI
  78. Li J, Zhao WX, Wen JR, et al (2019a) Generating long and informative reviews with aspect-aware coarse-to-fine decoding. In: ACL, https://doi. org/10.18653/v1/P19-1190 · DOI
  79. Li J, Sato A, Shimura K, et al (2020) Multi-task peer-review score prediction. In: SDP, https:// doi.org/10.18653/v1/2020.sdp-1.14 · DOI
  80. Li P, Jiang X, Shatkay H (2019b) Figure and caption extraction from biomedical documents. Bioinformatics 35(21):4381-4388. https://doi. org/10.1093/bioinformatics/btz228 · DOI
  81. Li Y, Choi D, Chung J, et al (2022) Competitionlevel code generation with AlphaCode. Science 378(6624):1092-1097. https://doi.org/10.1126/ science.abq1158
  82. Lin J, Yu Y, Zhou Y, et al (2020) How many preprints have actually been printed and why: A case study of computer science preprints on arXiv. Scientometrics 124(1):555-574. https:// doi.org/10.1007/s11192-020-03430-8 · DOI
  83. Liu ZR, Liu Y (2017) Exploiting unlabeled data for neural grammatical error detection. Journal of Computer Science and Technology 32(4):758-767. https://doi.org/10.1007/ s11390-017-1757-4
  84. Lopez P (2009) GROBID: Combining automatic bibliographic data recognition and term extraction for scholarship publications. In: ECDL, https://doi.org/10.1007/978-3-642-04346-8 62 · DOI
  85. Lu X, Liu J (2014) An XML-based model method for review of academic dissertation format. In: ISCID, https://doi.org/10.1109/ISCID.2014.68 · DOI
  86. Ma A, Liu Y, Xu X, et al (2021) A deep-learning based citation count prediction model with paper metadata semantic features. Scientometrics 126(8):6803-6823. https://doi.org/10.1007/ s11192-021-04033-7
  87. Ma S, Zhang C, Liu X (2020) A review of citation recommendation: From textual content to enriched context. Scientometrics 122(3):1445-1472. https://doi.org/10.1007/ s11192-019-03336-0
  88. Mansouri B (2021) Embedding formulae and text for improved math retrieval. In: SIGIR, https: //doi.org/10.1145/3404835.3463264 · DOI
  89. Marrese-Taylor E, Loyola P, Matsuo Y (2019) An edit-centric approach for Wikipedia article quality assessment. In: EMNLP W-NUT, https://doi.org/10.18653/v1/D19-5550 · DOI
  90. Marsh HW, Jayasinghe UW, Bond NW (2008) Improving the peer-review process for grant applications: Reliability, validity, bias, and generalizability. American Psychologist 63(3):160-168. https: //doi.org/10.1037/0003-066X.63.3.160 · DOI · Paper page
  91. Matsumoto K, Shibayama S, Kang B, et al (2021) Introducing a novelty indicator for scientific research: Validating the knowledgebased combinatorial approach. Scientometrics 126(8):6891-6915. https://doi.org/10.1007/ s11192-021-04049-z
  92. Meng R, Thaker K, Zhang L, et al (2021) Bringing structure into summaries: A faceted summarization dataset for long scientific documents. In: ACL Short Papers, https://doi.org/10.18653/ v1/2021.acl-short.137
  93. Meuschke N (2021) Analyzing non-textual content elements to detect academic plagiarism. Thesis, University of Konstanz, https://doi.org/10. 5281/zenodo.4913345
  94. Mikolov T, Chen K, Corrado G, et al (2013) Efficient estimation of word representations in vector space. In: ICLR
  95. Mohamed M, Oussalah M (2019) SRL-ESATextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis. Information Processing & Management 56(4):1356-1372. https://doi.org/ 10.1016/j.ipm.2019.04.003 · DOI
  96. Mrowinski MJ, Fronczak P, Fronczak A, et al (2017) Artificial intelligence in peer review: How can evolutionary computation support journal editors? PLOS ONE 12(9):e0184,711. https:// doi.org/10.1371/journal.pone.0184711 · DOI
  97. Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Belknap Press of Harvard University Press
  98. Ng HT, Wu SM, Briscoe T, et al (2014) The CoNLL-2014 shared task on grammatical error correction. In: CoNLL Shared Task, https:// doi.org/10.3115/V1/W14-1701 · DOI
  99. Nguyen-Son HQ, Tieu NDT, Nguyen HH, et al (2017) Identifying computer-generated text using statistical analysis. In: APSIPA ASC, https://doi.org/10.1109/APSIPA.2017.8282270 · DOI · Paper page
  100. Nigam K, McCallum AK, Thrun S, et al (2000) Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2):103-134. https://doi.org/10.1023/A: 1007692713085 · DOI
  101. Nuijten MB, Polanin JR (2020) 'statcheck': Automatically detect statistical reporting inconsistencies to increase reproducibility of meta-analyses. Research Synthesis Methods 11(5):574-579. https://doi.org/10.1002/jrsm. 1408 · DOI
  102. Osman AH, Salim N, Binwahlan MS, et al (2012) An improved plagiarism detection scheme based on semantic role labeling. Applied Soft Computing 12(5):1493-1502. https://doi.org/10.1016/j. asoc.2011.12.021 · DOI
  103. Page EB (1966) The imminence of grading essays by computer. The Phi Delta Kappan 47(5):238243
  104. Park LA, Simoff S (2014) Second order probabilistic models for within-document novelty detection in academic articles. In: SIGIR, https: //doi.org/10.1145/2600428.2609520 · DOI
  105. Paullada A, Raji ID, Bender EM, et al (2021) Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns 2(11):100,336. https://doi. org/10.1016/j.patter.2021.100336 · DOI · Paper page
  106. Peng H, Li J, He Y, et al (2018) Large-scale hierarchical text classification with recursively regularized deep Graph-CNN. In: The Web Conference, https://doi.org/10.1145/3178876.3186005 · DOI
  107. Peng S, Yuan K, Gao L, et al (2021) MathBERT: A pre-trained model for mathematical formula understanding. arXiv preprint arXiv:2105.00377 · arXiv
  108. Pennington J, Socher R, Manning C (2014) GloVe: Global vectors for word representation. In: EMNLP, https://doi.org/10.3115/v1/D14-1162 · DOI · Paper page
  109. Pilault J, Li R, Subramanian S, et al (2020) On extractive and abstractive neural document summarization with transformer language models. In: EMNLP, https://doi.org/10.18653/v1/ 2020.emnlp-main.748 · DOI
  110. Pradhan T, Pal S (2020) CNAVER: A content and network-based academic venue recommender system. Knowledge-Based Systems 189:105,092. https://doi.org/10.1016/j.knosys.2019.105092 · DOI
  111. Pradhan T, Kumar P, Pal S (2021a) CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation. Information Sciences 559:212-235. https://doi.org/ 10.1016/J.INS.2020.12.024 · DOI
  112. Pradhan T, Sahoo S, Singh U, et al (2021b) A proactive decision support system for reviewer recommendation in academia. Expert Systems with Applications 169:114,331. https://doi.org/ 10.1016/j.eswa.2020.114331 · DOI
  113. Price S, Flach PA (2017) Computational support for academic peer review: A perspective from artificial intelligence. Communications of the ACM 60(3):70-79. https://doi.org/10.1145/ 2979672
  114. Qiao F, Xu L, Han X (2018) Modularized and attention-based recurrent convolutional neural network for automatic academic paper aspect scoring. In: WISA, https://doi.org/10.1007/ 978-3-030-02934-0 7
  115. Qiu X, Sun T, Xu Y, et al (2020) Pretrained models for natural language processing: A survey. Science China Technological Sciences 63(10):1872-1897. https://doi.org/10. 1007/s11431-020-1647-3
  116. Rai A, Constantinides P, Sarker S (2019) Nextgeneration digital platforms: Toward human-AI hybrids. MIS Quarterly 43(1):iii-ix
  117. Ramesh D, Sanampudi SK (2022) An automated essay scoring systems: A systematic literature review. Artificial Intelligence Review 55(3):2495-2527. https://doi.org/10. 1007/s10462-021-10068-2
  118. Rei M, Yannakoudakis H (2016) Compositional sequence labeling models for error detection in learner writing. In: ACL, https://doi.org/10. 18653/V1/P16-1112
  119. Ren K, Zheng T, Qin Z, et al (2020) Adversarial attacks and defenses in deep learning. Engineering 6(3):346-360. https://doi.org/10.1016/ j.eng.2019.12.012
  120. Roostaee M, Sadreddini MH, Fakhrahmad SM (2020) An effective approach to candidate retrieval for cross-language plagiarism detection: A fusion of conceptual and keyword-based schemes. Information Processing & Management 57(2):102,150. https://doi.org/10.1016/j. ipm.2019.102150 · DOI
  121. Ruan QZ, Chen AD, Cohen JB, et al (2018) Alternative metrics of scholarly output: The relationship among altmetric score, Mendeley reader score, citations, and downloads in Plastic and Reconstructive Surgery. Plastic and Reconstructive Surgery 141(3):801-809. https://doi. org/10.1097/prs.0000000000004128 · DOI
  122. Russell SJ, Norvig P (2021) Artificial intelligence: A modern approach (Global Edition), 4th edn. Pearson
  123. Sahi M, Gupta V (2017) A novel technique for detecting plagiarism in documents exploiting information sources. Cognitive Computation 9(6):852-867. https://doi.org/10.1007/ S12559-017-9502-4
  124. Sanchez LM, Franco-Penya H (2016) Combined Tree Kernel-based classifiers for assessing quality of scientific text. In: BEA, https://doi.org/ 10.18653/v1/W16-0525 · DOI
  125. Santos MS, Abreu PH, Japkowicz N, et al (2023) A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research. Information Fusion 89:228-253. https://doi.org/10.1016/j. inffus.2022.08.017 · DOI
  126. Schmaltz A, Kim Y, Rush AM, et al (2016) Sentence-level grammatical error identification as sequence-to-sequence correction. In: BEA, https://doi.org/10.18653/v1/W16-0528 https://doi.org/10.18653/v1/W16-0528 · DOI
  127. Schulz KF, Altman DG, Moher D (2010) CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ 340:c332. https://doi.org/10.1136/bmj.c332 · DOI · Paper page
  128. Schumpeter JA (1939) Business cycles: A theoretical historical and statistical analysis of the capitalist process. McGraw-Hill Book Company
  129. Percie du Sert N, Hurst V, Ahluwalia A, et al (2020) The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biology 18(7):e3000,410. https://doi.org/10. 1371/journal.pbio.3000410
  130. Shen A, Salehi B, Baldwin T, et al (2019) A joint model for multimodal document quality assessment. In: JCDL, https://doi.org/10.1109/ JCDL.2019.00024
  131. Shibayama S, Yin D, Matsumoto K (2021) Measuring novelty in science with word embedding. PLOS ONE 16(7):e0254,034. https://doi.org/ 10.1371/journal.pone.0254034 · DOI
  132. Silver D, Schrittwieser J, Simonyan K, et al (2017) Mastering the game of Go without human knowledge. Nature 550(7676):354-359. https:// doi.org/10.1038/nature24270 · DOI · Paper page
  133. Singh S, Singh M, Goyal P (2021) COMPARE: A taxonomy and dataset of comparison discussions in peer reviews. In: JCDL, https://doi. org/10.1109/JCDL52503.2021.00068 · DOI · Paper page
  134. Skorikov M, Momen S (2020) Machine learning approach to predicting the acceptance of academic papers. In: IAICT, https://doi.org/10. 1109/IAICT50021.2020.9172011
  135. Smith R (2006) Peer review: A flawed process at the heart of science and journals. Journal of the Royal Society of Medicine 99(4):178-182. https: //doi.org/10.1258/JRSM.99.4.178 · DOI · Paper page
  136. Song E, Ang L, Park JY, et al (2021) A scoping review on biomedical journal peer review guides for reviewers. PLOS ONE 16(5):e0251,440. https://doi.org/10.1371/journal.pone.0251440 · DOI
  137. Stappen L, Rizos G, Hasan M, et al (2020) Uncertainty-aware machine support for paper reviewing on the Interspeech 2019 Submission Corpus. In: Interspeech, https://doi.org/ 10.21437/Interspeech.2020-2862 · DOI · Paper page
  138. Storks S, Gao Q, Chai JY (2020) Recent advances in natural language inference: A survey of benchmarks, resources, and approaches. arXiv preprint arXiv:1904.01172 · arXiv
  139. Sun H, Linton JD (2014) Structuring papers for success: Making your paper more like a high impact publication than a desk reject. Technovation 34(10):571-573. https://doi.org/ 10.1016/j.technovation.2014.07.008 · DOI
  140. Tan C, Lee L (2014) A corpus of sentence-level revisions in academic writing: A step towards understanding statement strength in communication. In: ACL Short Papers, https://doi.org/ 10.3115/v1/P14-2066 · DOI
  141. Tanveer MI, Hassan MK, Gildea D, et al (2019) A causality-guided prediction of the TED talk ratings from the speech-transcripts using neural networks. arXiv preprint arXiv:1905.08392 · arXiv
  142. Uppal S, Bhagat S, Hazarika D, et al (2022) Multimodal research in vision and language: A review of current and emerging trends. Information Fusion 77:149-171. https://doi.org/10. 1016/j.inffus.2021.07.009
  143. Uzzi B, Mukherjee S, Stringer M, et al (2013) Atypical combinations and scientific impact. Science 342(6157):468-472. https://doi.org/10. 1126/SCIENCE.1240474
  144. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: NIPS
  145. Vincent-Lamarre P, Larivi` ere V (2021) Textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome. Quantitative Science Studies 2(2):662677. https://doi.org/10.1162/qss a 00125 · DOI
  146. Wang D, Liang Y, Xu D, et al (2018) A content-based recommender system for computer science publications. KnowledgeBased Systems 157:1-9. https://doi.org/10. 1016/j.knosys.2018.05.001
  147. Wang J (2016) Knowledge creation in collaboration networks: Effects of tie configuration. Research Policy 45(1):68-80. https://doi.org/ 10.1016/j.respol.2015.09.003 · DOI
  148. Wang Q, Tan Y (2020) Grammatical error detection with self attention by pairwise training. In: IJCNN, https://doi.org/10.1109/IJCNN48605. 2020.9206715 · DOI
  149. Wang Q, Zeng Q, Huang L, et al (2020) ReviewRobot: Explainable paper review generation based on knowledge synthesis. In: INLG
  150. Wang Z, Beyette D, Lin J, et al (2019) Extraction of math expressions from PDF documents based on unsupervised modeling of fonts. In: ICDAR, https://doi.org/10.1109/ICDAR.2019.00068 · DOI
  151. Ware M, Mabe M (2015) The STM Report: An overview of scientific and scholarly journal publishing, 4th edn. International Association of Scientific, Technical and Medical Publishers
  152. Whitelaw C, Hutchinson B, Chung GY, et al (2009) Using the web for language independent spellchecking and autocorrection. In: EMNLP, https://doi.org/10.3115/1699571.1699629 · DOI
  153. Yang P, Sun X, Li W, et al (2018) Automatic academic paper rating based on modularized hierarchical convolutional neural network. In: ACL
  154. Yang Z, Yang D, Dyer C, et al (2016) Hierarchical attention networks for document classification. In: NAACL-HLT, https://doi.org/10. 18653/v1/N16-1174
  155. Yasunaga M, Kasai J, Zhang R, et al (2019) ScisummNet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks. In: AAAI, https://doi.org/10.1609/aaai.v33i01.33017386 · DOI
  156. Yuan W, Liu P, Neubig G (2022) Can we automate scientific reviewing? Journal of Artificial Intelligence Research 75:171-212. https://doi.org/10. 1613/jair.1.12862
  157. Zamora EM, Pollock JJ, Zamora A (1981) The use of trigram analysis for spelling error detection. Information Processing & Management 17(6):305-316. https://doi.org/10.1016/ 0306-4573(81)90044-3
  158. Zhang C, Yang Z, He X, et al (2020a) Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing 14(3):478-493. https://doi.org/10.1109/JSTSP. 2020.2987728 · DOI
  159. Zhang L, Zhang S, Balog K (2019) Table2Vec: Neural word and entity embeddings for table population and retrieval. In: SIGIR, https:// doi.org/10.1145/3331184.3331333 · DOI
  160. Zhang WE, Sheng QZ, Alhazmi A, et al (2020b) Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology 11(3):Article 24. https://doi.org/ 10.1145/3374217 · DOI
  161. Zheng X, Burdick D, Popa L, et al (2021) Global Table Extractor (GTE): A framework for joint table identification and cell structure recognition using visual context. In: WACV, https: //doi.org/10.1109/WACV48630.2021.00074 · DOI
  162. Zhong L, Wu J, Li Q, et al (2023) A comprehensive survey on automatic knowledge graph construction. arXiv preprint arXiv:2302.05019 · arXiv

Cited by

Sorted by citations

Authors
Lin, J., Song, J., Zhou, Z., Chen, Y., & Shi, X.
Affiliations
Xiamen University (CN)
Corresponding author
Shi, Xiaodong
ORCID
Venue
Information Fusion
Publisher
Elsevier BV
Published
2023-10
Volume
98
Pages
101830
ISSN
1566-2535 (ISSN-L)
DOI
10.1016/j.inffus.2023.101830
OpenAlex
W4376126211
Cited by
37
References
24
Citations by year
2026: 11 · 2025: 13 · 2024: 9 · 2023: 4
FWCI
32.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Fusion (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
Keywords
peer review, artificial intelligence in peer review, scholarly publishing, document parsing, document representation, human-computer interaction, research evaluation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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