Shay B. Cohen

5.7k total citations · 1 hit paper
120 papers, 2.4k citations indexed

About

Shay B. Cohen is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shay B. Cohen has authored 120 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Artificial Intelligence, 9 papers in Molecular Biology and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shay B. Cohen's work include Topic Modeling (78 papers), Natural Language Processing Techniques (76 papers) and Advanced Text Analysis Techniques (12 papers). Shay B. Cohen is often cited by papers focused on Topic Modeling (78 papers), Natural Language Processing Techniques (76 papers) and Advanced Text Analysis Techniques (12 papers). Shay B. Cohen collaborates with scholars based in United Kingdom, United States and Israel. Shay B. Cohen's co-authors include Shashi Narayan, Mirella Lapata, Noah A. Smith, Yumo Xu, Marco Damonte, Anastasia Shimorina, Claire Gardent, Eytan Ruppin, Gideon Dror and Giorgio Satta and has published in prestigious journals such as PLoS ONE, Solar Energy and Neural Computation.

In The Last Decade

Shay B. Cohen

110 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

2018 639 citations Shashi Narayan, Shay B. Cohen et al. Edinburgh Research Explorer (University of Edinburgh) profile →

Peers

Shay B. Cohen

AI

CVPR

MSOR

IS

MB

Yukun Ma Singapore

Xiaowen Dong United Kingdom

Xu Sun China

Tyler Derr United States

Linyi Yang China

Katarzyna Musiał Australia

Ting Liu China

Sen Wu China

Fabio Stella Italy

Yukun Ma Singapore

Shay B. Cohen

1.2k ×0.6

AI

216 ×0.8

CVPR

241 ×1.0

MSOR

191 ×1.2

IS

26 ×0.2

MB

Citations per year, relative to Shay B. Cohen Shay B. Cohen (= 1×) peers Yukun Ma

Countries citing papers authored by Shay B. Cohen

Since Specialization

Citations

This map shows the geographic impact of Shay B. Cohen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shay B. Cohen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shay B. Cohen more than expected).

Fields of papers citing papers by Shay B. Cohen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shay B. Cohen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shay B. Cohen. The network helps show where Shay B. Cohen may publish in the future.

Co-authorship network of co-authors of Shay B. Cohen

This figure shows the co-authorship network connecting the top 25 collaborators of Shay B. Cohen. A scholar is included among the top collaborators of Shay B. Cohen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shay B. Cohen. Shay B. Cohen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zhao, Zheng, et al.. (2025). PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants. 18023–18055.

2.

Gallé, Matthias, et al.. (2024). On the Trade-off between Redundancy and Cohesiveness in Extractive Summarization. Journal of Artificial Intelligence Research. 80. 273–326.

3.

Wang, Mengyu, Shay B. Cohen, & Tiejun Ma. (2024). Modeling News Interactions and Influence for Financial Market Prediction. Edinburgh Research Explorer (University of Edinburgh). 3302–3314.

4.

Cohen, Shay B., et al.. (2024). Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains. Edinburgh Research Explorer. 8027–8042.

5.

Zhao, Zheng, et al.. (2024). CivilSum: A Dataset for Abstractive Summarization of Indian Court Decisions. Edinburgh Research Explorer (University of Edinburgh). 2241–2250. 1 indexed citations

6.

Ziser, Yftah, et al.. (2023). Erasure of Unaligned Attributes from Neural Representations. Transactions of the Association for Computational Linguistics. 11. 488–510. 2 indexed citations

7.

Qiu, Yifu & Shay B. Cohen. (2022). Abstractive Summarization Guided by Latent Hierarchical Document Structure. 5303–5317. 5 indexed citations

8.

Damonte, Marco, et al.. (2020). The Role of Reentrancies in Abstract Meaning Representation Parsing. 2198–2207. 6 indexed citations

9.

Stanojević, Miloš, et al.. (2019). Wide-Coverage Neural A* Parsing for Minimalist Grammars. Edinburgh Research Explorer. 2486–2505. 4 indexed citations

10.

Coavoux, Maximin, Shashi Narayan, & Shay B. Cohen. (2018). Privacy-preserving Neural Representations of Text. arXiv (Cornell University). 1–10. 61 indexed citations

11.

Narayan, Shashi, Shay B. Cohen, & Mirella Lapata. (2018). Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. Edinburgh Research Explorer (University of Edinburgh). 1797–1807. 639 indexed citations breakdown →

12.

Cohen, Shay B., et al.. (2018). Multilingual Clustering of Streaming News. Edinburgh Research Explorer. 4535–4544. 15 indexed citations

13.

Sokolov, Artem, Stefan Riezler, & Shay B. Cohen. (2015). Coactive learning for interactive machine translation. International Conference on Machine Learning. 41–45. 2 indexed citations

14.

Cohen, Shay B., Karl Stratos, Michael Collins, Dean P. Foster, & Lyle Ungar. (2014). Spectral learning of latent-variable PCFGs: algorithms and sample complexity. Journal of Machine Learning Research. 15(1). 2399–2449. 13 indexed citations

15.

Stratos, Karl, Alexander M. Rush, Shay B. Cohen, & Michael Collins. (2013). Spectral Learning of Refinement HMMs. Edinburgh Research Explorer (University of Edinburgh). 56–64. 8 indexed citations

16.

Collins, Michael & Shay B. Cohen. (2012). Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs. Neural Information Processing Systems. 25. 2519–2527. 21 indexed citations

17.

Cohen, Shay B., Karl Stratos, Michael Collins, Dean P. Foster, & Lyle Ungar. (2012). Spectral Learning of Latent-Variable PCFGs. Edinburgh Research Explorer (University of Edinburgh). 1. 223–231. 11 indexed citations

18.

Cohen, Shay B., Carlos Gómez‐Rodríguez, & Giorgio Satta. (2011). Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 12 indexed citations

19.

Cohen, Shay B., Carlos Gómez‐Rodríguez, & Giorgio Satta. (2011). Exact Inference for Generative Probabilistic Non-Projective Dependency Parsing. Edinburgh Research Explorer (University of Edinburgh). 1234–1245. 14 indexed citations

20.

Cohen, Shay B. & Noah A. Smith. (2010). Covariance in Unsupervised Learning of Probabilistic Grammars. Journal of Machine Learning Research. 11(101). 3017–3051. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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