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REVIEW 3 major objections 6 minor 103 references

A Structured Literature Review on Traditional Approaches in Current Natural Language Processing

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that traditional NLP models—SVMs, rule-based extractors, extractive summarizers—still appear across all five surveyed tasks, in 28 of 119 papers.

desk verdict A transparent, useful SLR snapshot whose abstract overclaims the text-simplification evidence and whose 2023 tables quietly include 2022/2024 papers — fixable, and worth refereeing. read the letter →

arxiv 2505.12970 v1 pith:Q5UWPJ7L submitted 2025-05-19 cs.CL

Robin Jegan , Andreas Henrich This is my paper · ORCID
classification cs.CL
keywords traditionalNLPmodelslargelanguagestructuredliteraturereviewtextclassificationinformationextractionrelationsimplificationsummarization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Large language models dominate current NLP, but this paper asks whether the older, simpler techniques they displaced have actually disappeared. Surveying 119 papers published in one year across classification, information extraction, relation extraction, text simplification, and text summarization, it finds that 28 of them still use traditional models—support vector machines, naive Bayes, decision trees, rule-based extraction, or extractive summarization—either inside a processing pipeline, as comparison baselines, or as the main method. The paper's central claim is that traditional techniques remain live options in today's research, chosen deliberately for reproducibility, efficiency, and explainability rather than kept only as historical artifacts. That matters because it complicates the story of a wholesale neural takeover and points to concrete situations where simpler methods still earn their place.

What carries the argument

The survey works by fixing an operational definition of "traditional" and applying a three-way usage taxonomy to each paper. A model counts as traditional when it is deterministic and reproducible, cheap in time and hardware, and well documented or explainable; concretely this means SVMs, naive Bayes, decision trees, n-gram or TF-IDF features, rule-based extraction, or extractive summarization algorithms. The authors then classify each relevant paper by whether the traditional component sits in the pipeline, serves as comparison or baseline, or acts as the core model. This definition-and-taxonomy machinery is what turns scattered examples into a quantitative claim with a clear answer to each research question.

What would settle it

Re-running the survey on a different database, a different year, or broader search terms to see whether traditional models still appear as core methods would settle it; for instance, if a comparable sample from 2025 proceedings shows zero or near-zero papers using rule-based, SVM, or extractive methods as the main approach outside a few low-resource domains, the paper's generalization holds only for its narrow snapshot. A sharper check is to take 50 recent relation-extraction papers from venues outside the original database and count how many use SVM, k-nearest neighbors, or hand-written rules as the main method; the paper's claim predicts several, and finding none would falsify it.

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Extended reading notes

Core claim

Within the surveyed snapshot, every one of the five application scenarios contains papers using traditional approaches. Summarization shows the strongest presence, with extractive algorithms such as LexRank, TextRank, and lead-based selection appearing in over half of the retrieved papers, often as baselines or hybrid components; classification relies mainly on SVM and naive Bayes as comparisons; information extraction and relation extraction use rule-based systems or SVM/kNN as core methods in narrow domains; text simplification is the scarcest, surfacing only in lexical-complexity prediction. The authors interpret the pattern as evidence that traditional models persist in three clearly distinguishable roles—pipeline parts, baselines, and main models—and that their persistence is tied to properties LLMs do not reliably offer: deterministic repeatability, low compute cost, documentation, and freedom from hallucination in extractive settings.

Load-bearing premise

The paper's conclusion rests on treating one year's papers from a single bibliographic database with exact-title searches as a fair sample of current NLP research; if that sample is unrepresentative, the claim that traditional models remain broadly relevant does not follow.

Editorial extensions

If this is right

  • Evaluation practice should keep traditional baselines, because current papers still measure newer models against SVM, naive Bayes, and extractive systems.
  • Hybrid systems are a live design pattern: transformer models and large language models are combined with extractive selection or rule-based extraction in current work.
  • For hallucination-sensitive settings such as legal texts, news, or low-resource languages, extractive and rule-based methods remain a defensible choice that some surveyed papers explicitly motivate.
  • The uneven distribution—summarization rich in traditional methods, text simplification nearly absent—implies that continued relevance is task-dependent rather than uniform across NLP.
  • If traditional models still serve as reference points, then claims that a task is solved based only on LLM-versus-LLM comparisons are incomplete.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: the paper's own data suggest the traditional-versus-modern boundary is not a timeline but a design trade-off, so rising energy and API costs could strengthen the case for deterministic low-cost methods in production.
  • Editorial extension: a testable follow-up would be to measure whether traditional methods cluster in low-resource languages or narrowly scoped domains; if so, they may be the default wherever annotated data or compute is scarce.
  • Editorial extension: because the paper reports usage rather than performance, one could extend it by collecting the relative scores of traditional baselines to identify when simplicity actually matches or beats modern models.
  • Editorial extension: since the search was limited to exact phrases in titles, the true prevalence of traditional methods may be higher than the reported counts, making the paper's figures a lower bound.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript reports a structured literature review of five NLP application scenarios (classification, information extraction, relation extraction, text simplification, and text summarization) based on publications retrieved from the ACM Digital Library, with a declared focus on 2023. The authors define "traditional models" pragmatically for each scenario, then classify retrieved papers according to three usage roles: part of a processing pipeline, comparison or baseline, or core method. They report that 28 of 119 retrieved papers incorporate traditional models and claim that all five application scenarios still exhibit traditional models in one of these roles. The paper also addresses why traditional approaches remain in use (RQ2) and what advantages they offer (RQ3), and includes a brief discussion of future perspectives.

Significance. If the universal claim were fully supported, the paper would be a useful, reproducible evidence point that traditional NLP methods remain relevant alongside neural and LLM-based approaches, with practical implications for reproducibility, efficiency, and domain-constrained applications. The study's strengths are its transparent search and classification protocol, public dataset on Zenodo, explicit terminology discussion, and an honest limitations section. The main caveat is that the evidence for one of the five scenarios (text simplification) is a single survey paper that is not classified as using traditional models in any of the three stated roles, and the sample includes papers outside the declared 2023 window. After aligning the claims with the actual evidence, the survey would be a worthwhile contribution; in its present form, the headline claim overstates the findings.

major comments (3)
  1. [Abstract, §4, §5, Table 2] The abstract's central claim that all five application scenarios "still exhibit traditional models" as part of a pipeline, as a comparison/baseline, or as the main model is not supported by the paper's own evidence for text simplification. The only retrieved text-simplification paper, North et al. [61], is a survey; Table 2 assigns it no checkmarks in any of the three usage columns, and §5 states only that "a survey on lexical complexity prediction is done." A survey that discusses classifiers is not an instance of a model being used in a pipeline, as a baseline, or as the core method. The authors should either revise the abstract and RQ1 answer to distinguish "discussed in the literature" from "used in the paper," or explicitly report that no direct usage evidence was found for text simplification.
  2. [§4, Tables 1–2] The methodology states that the pre-determined publication-year criterion is 2023, but the qualitative analysis includes several papers outside that window: [40], [50], [52], [65], [78], and [90] are from 2022, and [12] is from 2024. This contradicts the declared selection criterion and makes the claim about "current" NLP research ambiguous. If these papers were included intentionally (for example, to increase scenario coverage or because of publication-date conventions), the methodology must state this explicitly and report the inclusion criteria; otherwise the analysis should be restricted to 2023.
  3. [§4, §7, Figure 1] The universal conclusion "for all five application scenarios traditional models still exist" rests on very small per-scenario samples: one retrieved paper for text simplification and three for information extraction. The abstract's phrasing "in one way or another" obscures this sparsity and suggests equal evidentiary strength across scenarios. The authors should report per-scenario counts and confidence levels in the abstract and conclusion, and qualify the strength of the claim accordingly.
minor comments (6)
  1. [Abstract, §4 footnote] The abstract cites the complete statistics at https://zenodo.org/records/13683801, while the footnote in §4 cites https://doi.org/10.5281/zenodo.13683800; please unify the identifier so readers can locate the dataset.
  2. [§4] The sentence claiming that "nearly every retrieved publication has undergone a peer review process" is stronger than the cited 80% peer-reviewed figure supports; please rephrase to "the large majority" or similar.
  3. [Table 2] The column headers "Compe." and "Tr. Be." are undefined; please spell them out as "Competitive" and "Trailing Behind" so the reader can interpret the table without referring back to the caption.
  4. [§5, Table 2] The usage category "comparison/baseline" conflates two distinct functions. If the coding scheme is kept, please clarify whether a paper counts as comparison even when the traditional model is not presented as a baseline in the experimental design.
  5. [§6] The answer to RQ3 is inferred by the authors from the selected models' properties rather than drawn from explicit discussion in the retrieved papers; this should be labeled as the authors' interpretation, not as a finding of the SLR.
  6. [Throughout] Please fix typographical errors such as "Furtermore" (§2.3), "text simplificationas well astext summarization" (Abstract), and "applications scenarios" (§7).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's conclusions are empirical counts, not derivations; the abstract's text-simplification overstatement is an internal-evidence flaw, not a circular one.

full rationale

This paper is a structured literature review, not a derivation. Its central claim—that traditional models still appear in 2023 NLP papers—is an empirical summary of the papers retrieved from ACM DL and classified in Tables 1 and 2. The definition of 'traditional' in Section 3 is admittedly pragmatic, and the authors state in Section 8 that it 'could be scrutinized'; this affects scope and external validity, but does not make the finding equivalent to its inputs by construction. The search protocol was broadened for text simplification, and the retrieved paper is a survey whose table entry has no checkmarks, so the abstract's universal phrasing ('all five application scenarios still exhibit traditional models ... as part of a processing pipeline, as a comparison/baseline ... or as the main model(s)') is not supported by the paper's own Table 2 for text simplification. That is an internal-consistency/correctness problem, not circularity: the conclusion is not forced by the definition of 'traditional' or by a self-citation; a contrary result (zero papers, or no applicable usage) was possible under the stated protocol. No fitted parameters, imported uniqueness theorems, or self-citation chains are load-bearing. Score 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no fitted parameters or new theoretical entities. Its conclusions rest on methodological assumptions about the representativeness of the ACM DL sample, the adequacy of the search strategy, the task-specific definition of 'traditional', and the consistency of manual classification.

assumptions (4)
  • domain assumption ACM DL is a sufficiently representative database for high-quality NLP research.
    The authors choose ACM DL over ACL Anthology or arXiv to ensure peer review, but this restricts coverage to a subset of NLP venues. The generality of the conclusion depends on this representativeness. (Section 4)
  • domain assumption Exact-title search queries are adequate to retrieve relevant papers for the five scenarios.
    The search is based on title matches only, except text simplification where abstract search is added. This may miss relevant papers whose titles do not contain the exact scenario terms. (Section 4)
  • ad hoc to paper The task-dependent definition of 'traditional models' is coherent enough for cross-scenario comparison.
    The authors define traditional models per scenario in Section 3 with acknowledged pragmatism. The claim that all five scenarios show traditional models relies on these varying definitions. (Section 3)
  • domain assumption Manual classification of papers into pipeline, comparison, or core is consistent.
    The classification in Table 2 is performed by the authors without an inter-rater reliability check. The quantitative statements depend on this subjective coding. (Section 5 and Table 2)

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Cite this review

Pith. "Pith review of A Structured Literature Review on Traditional Approaches in Current Natural Language Processing." pith.science (2026). https://pith.science/paper/Q5UWPJ7L

@misc{pith2026250512970,
  author       = {Pith},
  title        = {Pith review of: A Structured Literature Review on Traditional Approaches in Current Natural Language Processing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q5UWPJ7L}},
  note         = {Machine review of arXiv:2505.12970}
}
read the original abstract

The continued rise of neural networks and large language models in the more recent past has altered the natural language processing landscape, enabling new approaches towards typical language tasks and achieving mainstream success. Despite the huge success of large language models, many disadvantages still remain and through this work we assess the state of the art in five application scenarios with a particular focus on the future perspectives and sensible application scenarios of traditional and older approaches and techniques. In this paper we survey recent publications in the application scenarios classification, information and relation extraction, text simplification as well as text summarization. After defining our terminology, i.e., which features are characteristic for traditional techniques in our interpretation for the five scenarios, we survey if such traditional approaches are still being used, and if so, in what way they are used. It turns out that all five application scenarios still exhibit traditional models in one way or another, as part of a processing pipeline, as a comparison/baseline to the core model of the respective paper, or as the main model(s) of the paper. For the complete statistics, see https://zenodo.org/records/13683801

Figures

Figures reproduced from arXiv: 2505.12970 by the authors.

Figure 1
Figure 1. Results of the SLR, “Traditional Papers” [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

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Reference graph

Works this paper leans on

103 extracted references · 68 canonical work pages

  1. [61]

    Faiza Mehmood, Rehab Shahzadi, Hina Ghafoor, Muhammad Nabeel Asim, Muhammad Usman Ghani, Waqar Mahmood, and Andreas Dengel. 2023. EnML: Multi-label Ensemble Learning for Urdu Text Classification. ACM Transactions on Asian and Low- Resource Language Information Processing, 22(9):1– 31

    work page 2023

  2. [40]

    Khang Nhut Lam, Tai Ngoc Nguyen, and Jugal Kalita. 2022. Vietnamese Text Summarization Based on Elementary Discourse Units. In Proceedings of the 2022 6th International Conference on Nat- ural Language Processing and Information Retrieval, pages 1–5, Bangkok Thailand. ACM

    work page 2022

  3. [50]

    Daniele Liberatore, Kyriaki Kalimeri, Derya Sever, and Yelena Mejova. 2024. Quantitative information extraction from humanitarian documents. In Pro- ceedings of the 2024 International Conference on Information Technology for Social Good, pages 240– 248

    work page 2024

  4. [52]

    Kevin Lin. 2023. Decision Trees for Text Classifi- cation in CS2. ACM, New York, NY , USA

    work page 2023

  5. [65]

    Afrodite Papagiannopoulou and C. Angeli. 2022. Social Media Text Summarisation Techniques and Approaches: A Literature Review. In Proceedings of the 2022 5th International Conference on Algo- rithms, Computing and Artificial Intelligence, pages 1–6, Sanya China. ACM

    work page 2022

  6. [78]

    Harley, Gavin Adamson, and Asmaa Malik

    Tabassum Sultana, Eric R. Harley, Gavin Adamson, and Asmaa Malik. 2022. Extracting Source Infor- mation From News Articles: Information Extraction. In Proceedings of the 2022 6th International Con- ference on Natural Language Processing and Infor- mation Retrieval, pages 216–221, Bangkok Thailand. ACM

    work page 2022

  7. [90]

    Jiahui Wang, Kun Yue, and Liang Duan. 2023. Models and techniques for domain relation extraction: a survey. Journal of Data Science and Intelligent Sys- tems, 1(2):65–82

    work page 2023

  8. [12]

    Huiyao Chen, Yueheng Sun, Meishan Zhang, and Min Zhang. 2024. Automatic Noise Generation and Reduction for Text Classification. IEEE/ACM Trans- actions on Audio, Speech, and Language Processing, 32:139–150

    2024

Show all 103 references
  1. [1]

    Charu C Aggarwal and ChengXiang Zhai. 2012. A survey of text classification algorithms. Mining text data, pages 163–222

    2012

  2. [2]

    Suha S Al-Thanyyan and Aqil M Azmi. 2021. Auto- mated text simplification: a survey. ACM Computing Surveys (CSUR), 54(2):1–36

    2021

  3. [3]

    Wejdan Alkaldi and Diana Inkpen. 2023. Text sim- plification to specific readability levels. Mathematics, 11(9):2063

    2023

  4. [4]

    Miriam Anschütz, Joshua Oehms, Thomas Wimmer, Bartłomiej Jezierski, and Georg Groh. 2023. Lan- guage models for german text simplification: Over- coming parallel data scarcity through style-specific pre-training. arXiv preprint arXiv:2305.12908

    2023 arXiv

  5. [5]

    Berk Atil, Alexa Chittams, Liseng Fu, Ferhan Ture, Lixinyu Xu, and Breck Baldwin. 2024. Llm stabil- ity: A detailed analysis with some surprises. arXiv preprint arXiv:2408.04667

    2024 arXiv

  6. [6]

    Yash Kumar Atri, Vikram Goyal, and Tanmoy Chakraborty. 2023. Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pa...

    2023

  7. [7]

    Guangsheng Bao and Yue Zhang. 2023. A Gen- eral Contextualized Rewriting Framework for Text Summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31:1624–1635

    2023

  8. [8]

    Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152

    1992

  9. [9]

    Leo Breiman. 2017. Classification and regression trees. Routledge

    2017

  10. [10]

    Eric Brill. 1992. A simple rule-based part of speech tagger. In Speech and Natural Language: Proceed- ings of a Workshop Held at Harriman, New York, February 23-26, 1992

    1992

  11. [11]

    Raman Chandrasekar, Christine Doran, and Srini- vas Bangalore. 1996. Motivations and methods for text simplification. In COLING 1996 Volume 2: The 16th International Conference on Computational Lin- guistics

    1996

  12. [13]

    Liam Cripwell, Joël Legrand, and Claire Gardent

  13. [14]

    Washington Cunha, Felipe Viegas, Celso França, Thierson Rosa, Leonardo Rocha, and Marcos An- dré Gonçalves. 2023. A Comparative Survey of In- stance Selection Methods applied to Non-Neural and Transformer-Based Text Classification. ACM Com- puting Surveys, 55(13s):1–52. 10

    2023

  14. [15]

    Jan De Belder and Marie-Francine Moens. 2010. Text simplification for children. In Proceedings of the SIGIR workshop on accessible search systems , pages 19–26

    2010

  15. [16]

    Kartik Detroja, CK Bhensdadia, and Brijesh S Bhatt

  16. [17]

    Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu. 2021. Few-NERD: A few-shot named entity recognition dataset. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Internatio...

    2021

  17. [18]

    Intelligent Systems with Applications, 19:200244

    A survey on relation extraction. Intelligent Systems with Applications, 19:200244

  18. [19]

    Kai Du, Guoming Lu, and Ke Qin. 2023. An Ex- tractive Text Summarization Based on Reinforcement Learning. In 2023 The 6th International Conference on Software Engineering and Information Manage- ment, pages 19–25, Palmerston North New Zealand. ACM

    2023

  19. [20]

    Jovi D’Silva and Uzzal Sharma. 2023. Impact of Similarity Measures in Graph-based Automatic Text Summarization of Konkani Texts. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2):1–16

    2023

  20. [21]

    Harold P Edmundson. 1969. New methods in au- tomatic extracting. Journal of the ACM (JACM) , 16(2):264–285

    1969

  21. [22]

    José Marcio Duarte and Lilian Berton. 2023. A review of semi-supervised learning for text classifica- tion. Artificial intelligence review, 56(9):9401–9469

    2023

  22. [23]

    Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelli- gence research, 22:457–479

    2004

  23. [24]

    Wafaa S El-Kassas, Cherif R Salama, Ahmed A Rafea, and Hoda K Mohamed. 2021. Automatic text summarization: A comprehensive survey. Expert systems with applications, 165:113679

    2021

  24. [25]

    Andrea Gasparetto, Matteo Marcuzzo, Alessandro Zangari, and Andrea Albarelli. 2022. A survey on text classification algorithms: From text to predic- tions. Information, 13(2):83

    2022

  25. [26]

    Mahak Gambhir and Vishal Gupta. 2017. Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 47(1):1–66

    2017

  26. [27]

    Ralph Grishman. 2015. Information extraction. IEEE Intelligent Systems, 30(5):8–15

    2015

  27. [28]

    Ralph Grishman. 2012. Information extraction: Capabilities and challenges. International Winter School in Language and Speech Technologies WSLST, 41

    2012

  28. [29]

    Rajarshi Haldar and Julia Hockenmaier. 2024. An- alyzing the performance of large language mod- els on code summarization. arXiv preprint arXiv:2404.08018

    2024 arXiv

  29. [30]

    Pooja Gupta, Swati Nigam, and Rajiv Singh. 2024. Automatic extractive text summarization using multi- ple linguistic features. ACM Transactions on Asian and Low-Resource Language Information Process- ing

    2024

  30. [31]

    Tin Kam Ho. 1995. Random decision forests. In Proceedings of 3rd international conference on doc- ument analysis and recognition , volume 1, pages 278–282. IEEE

    1995

  31. [32]

    Ridong Han, Tao Peng, Chaohao Yang, Benyou Wang, Lu Liu, and Xiang Wan. 2023. Is information extraction solved by chatgpt? an analysis of per- formance, evaluation criteria, robustness and errors. arXiv preprint arXiv:2305.14450

    2023 arXiv

  32. [33]

    Dat Hong, Tong Wang, and Stephen Baek. 2023. Protorynet - interpretable text classification via pro- totype trajectories. J. Mach. Learn. Res., 24(1)

    2023

  33. [34]

    Jerry R Hobbs. 1978. Resolving pronoun refer- ences. Lingua, 44(4):311–338

    1978

  34. [35]

    I Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, et al. 2021. Degree: A data-efficient generation-based event extraction model. arXiv preprint arXiv:2108.12724

    2021 arXiv

  35. [36]

    Eduard Hovy and Chin-Yew Lin. 1998. Automated text summarization and the summarist system. In TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998, pages 197–214

    1998

  36. [37]

    Behrooz Janfada and Behrouz Minaei-Bidgoli

  37. [38]

    Xuming Hu, Junzhe Chen, Shiao Meng, Lijie Wen, and Philip S. Yu. 2023. SelfLRE: Self-refining Repre- sentation Learning for Low-resource Relation Extrac- tion. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, p...

    2023

  38. [39]

    TG Keerthan Kumar, Adhith Adhith, Nitin Jayadeep, and Shashidhar G Koolagudi. 2024. Syn- tactic simplification of english sentences using parse trees. In Proceedings of the 2024 Sixteenth Inter- national Conference on Contemporary Computing, pages 490–496. 11

    2024

  39. [41]

    Ammar Ismael Kadhim. 2019. Survey on super- vised machine learning techniques for automatic text classification. Artificial intelligence review , 52(1):273–292

    2019

  40. [42]

    Bo Li, Wei Ye, Jinglei Zhang, and Shikun Zhang

  41. [43]

    Jinpeng Li, Jiaze Chen, Huadong Chen, Dongyan Zhao, and Rui Yan. 2024. Multilingual genera- tion in abstractive summarization: A comparative study. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-C...

    2024

  42. [44]

    Bo Li, Gexiang Fang, Yang Yang, Quansen Wang, Wei Ye, Wen Zhao, and Shikun Zhang. 2023. Eval- uating chatgpt’s information extraction capabili- ties: An assessment of performance, explainabil- ity, calibration, and faithfulness. arXiv preprint arXiv:2304.11633

    2023 arXiv

  43. [45]

    Taiji Li, Zhi Li, and Yin Zhang. 2024. Improving faithfulness of large language models in summariza- tion via sliding generation and self-consistency. In Proceedings of the 2024 Joint International Con- ference on Computational Linguistics, Language Resources and Evaluation (L...

    2024

  44. [46]

    In Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, volume 37 (11), pages 13051–13058

    Reviewing labels: Label graph network with top-k prediction set for relation extraction. In Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, volume 37 (11), pages 13051–13058

  45. [47]

    Daniele Licari, Praveen Bushipaka, Gabriele Marino, Giovanni Comandé, and Tommaso Cu- cinotta. 2023. Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization. In Proceedings of the Nineteenth International Conference on Artificial Intell...

    2023

  46. [48]

    Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S Yu, and Lifang He. 2022. A survey on text classification: From traditional to deep learning. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2):1– 41

    2022

  47. [49]

    Lijuan Liu. 2023. Information extraction method of topic webpage based on multi-angle feature learning. In Proceedings of the 2023 4th International Confer- ence on Computing, Networks and Internet of Things, pages 92–96, Xiamen China. ACM

    2023

  48. [51]

    Hans Peter Luhn. 1958. The automatic creation of literature abstracts. IBM Journal of research and development, 2(2):159–165

    1958

  49. [53]

    Haoran Lv, Junyi Liu, Henan Wang, Yaoming Wang, Jixiang Luo, and Yaxiao Liu. 2023. Efficient hy- brid generation framework for aspect-based senti- ment analysis. In Proceedings of the 17th Confer- ence of the European Chapter of the Association for Computational Linguistics, p...

    2023

  50. [54]

    Shaoshuai Lu, Long Chen, Wenjing Wang, Cai Xu, Wei Zhao, Ziyu Guan, and Guangyue Lu. 2022. A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification. In Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recogni...

    2022

  51. [55]

    Paik, and Anupam Basu

    Debjani Mazumder, Jiaul H. Paik, and Anupam Basu. 2023. A Graph Neural Network Model for Concept Prerequisite Relation Extraction. In Pro- ceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 1787–1796, Birmingham United Kingdom. ACM

    2023

  52. [56]

    Chao Luo, Zi Chen, Xiaolin Jiang, and Sen Yang

  53. [57]

    Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing, pages 404–411

    2004

  54. [58]

    George A Miller. 1995. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39– 41

    1995

  55. [59]

    Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, and Quan Z Sheng. 2022. Multi-document summarization via deep learning techniques: A sur- vey. ACM Computing Surveys, 55(5):1–37

    2022

  56. [60]

    Frederick Mosteller and David L Wallace. 1963. Inference in an authorship problem: A comparative study of discrimination methods applied to the author- ship of the disputed federalist papers. Journal of the American Statistical Association, 58(302):275–309. 12

    1963

  57. [62]

    Khaled Omar and Mohamad Al-Shaar. 2023. Method for Arabic text Summarization using statisti- cal features and word2vector approach. In Proceed- ings of the 2023 9th International Conference on Computer Technology Applications, pages 258–262, Vienna Austria. ACM

    2023

  58. [63]

    Taha Osman, Hussein Khalil, Mohammed Miltan, Khaled Shaalan, and Rowida Alfrjani. 2023. Ex- ploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hy- brid Semantic Knowledge Base - Machine Learning Approach. ACM Transactions on Asian and L...

    2023

  59. [64]

    Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021. Deep learning–based text classification: a comprehensive review. ACM computing surveys (CSUR), 54(3):1–40

    2021

  60. [66]

    Kai North, Marcos Zampieri, and Matthew Shard- low. 2023. Lexical Complexity Prediction: An Overview. ACM Computing Surveys, 55(9):1–42

    2023

  61. [67]

    Min Su Park and Eunil Park. 2023. ACL TA-DA: A Dataset for Text Summarization and Generation. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing , pages 1233–1239, Tallinn Estonia. ACM

    2023

  62. [68]

    Max Peeperkorn, Tom Kouwenhoven, Dan Brown, and Anna Jordanous. 2024. Is temperature the cre- ativity parameter of large language models? arXiv preprint arXiv:2405.00492

    2024 arXiv

  63. [69]

    Ronghui Pang. 2024. Research on text classifica- tion applications based on nlp technology. In Pro- ceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things, pages 108–111

    2024

  64. [70]

    Sudipta Singha Roy and Robert E Mercer. 2024. Enhancing scientific document summarization with research community perspective and background knowledge. In Proceedings of the 2024 Joint In- ternational Conference on Computational Linguis- tics, Language Resources and Evaluation...

    2024

  65. [71]

    Angelina Parfenova. 2024. Automating the informa- tion extraction from semi-structured interview tran- scripts. In Companion Proceedings of the ACM Web Conference 2024, pages 983–986

    2024

  66. [72]

    Yaning Shi, Lukun Wang, Chunpeng Tian, Rujia Wang, Jiaming Pei, Amir Hussian, and Ali Kashif Bashir. 2023. A Chinese Short Text Classification Method for Tax Audit Reports based on Word Im- portance and Syntactic Enhancement BERT. ACM Transactions on Asian and Low-Resource Lan...

    2023

  67. [73]

    Yuchen Shi, Guochao Jiang, Tian Qiu, and Deqing Yang. 2024. Agentre: An agent-based framework for navigating complex information landscapes in re- lation extraction. In Proceedings of the 33rd ACM International Conference on Information and Knowl- edge Management, pages 2045–2055

    2024

  68. [74]

    Jipeng Qiang, Feng Zhang, Yun Li, Yunhao Yuan, Yi Zhu, and Xindong Wu. 2023. Unsupervised sta- tistical text simplification using pre-trained language modeling for initialization. Frontiers of Computer Science, 17(1):171303

    2023

  69. [75]

    Karen Sparck Jones. 1972. A statistical interpreta- tion of term specificity and its application in retrieval. Journal of documentation, 28(1):11–21

    1972

  70. [76]

    Ammar Saeed, Hikmat Ullah Khan, Achyut Shankar, Talha Imran, Danish Khan, Muhammad Kamran, and Muhammad Attique Khan. 2023. Topic Modeling based Text Classification Regarding Islam- ophobia using Word Embedding and Transformers Techniques. ACM Transactions on Asian and Low- Re...

    2023

  71. [77]

    Dianbo Sui, Xiangrong Zeng, Yubo Chen, Kang Liu, and Jun Zhao. 2023. Joint entity and relation extraction with set prediction networks. IEEE Trans- actions on Neural Networks and Learning Systems

    2023

  72. [79]

    Mkululi Sikosana, Oluwaseun Ajao, and Sean Maudsley-Barton. 2024. A comparative study of hybrid models in health misinformation text classifi- cation. In Proceedings of the 4th International Work- shop on Open Challenges in Online Social Networks, pages 18–25

    2024

  73. [80]

    Ciprian-Octavian Truic˘a, Andrei-Ionu¸ t Stan, and Elena-Simona Apostol. 2023. Simplex: a lexical text simplification architecture. Neural Computing and Applications, 35(8):6265–6280

    2023

  74. [81]

    Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, and Josep Torrellas. 2024. To- wards greener llms: Bringing energy-efficiency to the forefront of llm inference. arXiv preprint arXiv:2403.20306

    2024 arXiv

  75. [82]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30

    2017

  76. [83]

    Somin Wadhwa, Silvio Amir, and Byron C Wallace

  77. [84]

    Renliang Sun, Wei Xu, and Xiaojun Wan. 2023. Teaching the pre-trained model to generate sim- ple texts for text simplification. arXiv preprint arXiv:2305.12463

    2023 arXiv

  78. [85]

    Tianle Wang, Zihan Wang, Weitang Liu, and Jingbo Shang. 2023. WOT-Class: Weakly Supervised Open- world Text Classification. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 2666–2675, Birm- ingham United Kingdom. ACM

    2023

  79. [86]

    Laura Vásquez-Rodríguez, Matthew Shardlow, Piotr Przybyła, and Sophia Ananiadou. 2023. Document-level text simplification with coherence evaluation. In Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability, pages 85–101. 13

    2023

  80. [87]

    Daichi Yamaguchi, Rei Miyata, Sayuka Shimada, and Satoshi Sato. 2023. Gauging the gap between human and machine text simplification through an- alytical evaluation of simplification strategies and errors. In Findings of the Association for Computa- tional Linguistics: EACL 202...

    2023

  81. [88]

    Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu- Nan Ning, and Li Yuan. 2023. Llm lies: Hallucina- tions are not bugs, but features as adversarial exam- ples. arXiv preprint arXiv:2310.01469

    2023 arXiv

  82. [89]

    In Proceedings of the conference

    Revisiting relation extraction in the era of large language models. In Proceedings of the conference. Association for Computational Linguistics. Meeting, volume 2023, page 15566. NIH Public Access

    2023

  83. [91]

    Dell Zhang, Murat Sensoy, Masoud Makrehchi, Bilyana Taneva-Popova, Lin Gui, and Yulan He

  84. [92]

    Cheng Xiang and Zheng Yangfei. 2023. A Rule- Based Unstructured Information Extraction Model for Announcements of Listed Companies’ Stock In- crease or Decrease. In Proceedings of the 2023 7th International Conference on Computing and Data Analysis, pages 28–34, Guiyang China. ACM

    2023

  85. [93]

    Qianqian Zhang, Mengdong Chen, and Lianzhong Liu. 2017. A review on entity relation extraction. In 2017 second international conference on mechanical, control and computer engineering (ICMCCE), pages 178–183. IEEE

    2017

  86. [94]

    Kang Zhao, Hua Xu, Yue Cheng, Xiaoteng Li, and Kai Gao. 2021. Representation iterative fusion based on heterogeneous graph neural network for joint en- tity and relation extraction. Knowledge-Based Sys- tems, 219:106888

    2021

  87. [95]

    Chenhan Yuan, Qianqian Xie, and Sophia Anani- adou. 2023. Zero-shot temporal relation extraction with chatgpt. arXiv preprint arXiv:2304.05454

    2023 arXiv

  88. [96]

    Yongchao Yuan, Shaoqing Lv, Zhiqiang Bao, and Kai Li. 2022. A Joint Model for Text Classification with BERT-BiLSTM and GCN. In Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition, pages 180–186, Xiamen China. ACM

    2022

  89. [98]

    In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3426–3429, Taipei Tai- wan

    Uncertainty Quantification for Text Classifica- tion. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3426–3429, Taipei Tai- wan. ACM

  90. [99]

    Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In In- ternational conference on machine learning, pages 11328–11339. PMLR

    2020

  91. [102]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223

    2023 arXiv

  92. [103]

    Yongxiang Zhou and Yanli Xu. 2023. Extractive text summarization based on comparative learning. In Proceedings of the 2023 3rd International Con- ference on Big Data, Artificial Intelligence and Risk Management, pages 28–32. 14

    2023

  93. [2020]

    In 2020 6th International Conference on Web Research (ICWR), pages 271–278

    A review of the most important studies on automated text simplification evaluation metrics. In 2020 6th International Conference on Web Research (ICWR), pages 271–278. IEEE

    2020

  94. [2022]

    In Proceedings of the 2022 5th International Conference on Algo- rithms, Computing and Artificial Intelligence, pages 1–5, Sanya China

    Gap Sentences Generation with TextRank for Chinese Text Summarization. In Proceedings of the 2022 5th International Conference on Algo- rithms, Computing and Artificial Intelligence, pages 1–5, Sanya China. ACM

    2022

  95. [2023]

    In 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 993–1006

    Document-level planning for text simplifica- tion. In 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 993–1006. Association for Computational Linguis- tics

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.