Shashi Narayan

5.7k total citations · 1 hit paper
32 papers, 1.5k citations indexed

About

Shashi Narayan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Shashi Narayan has authored 32 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Shashi Narayan's work include Natural Language Processing Techniques (23 papers), Topic Modeling (22 papers) and Advanced Text Analysis Techniques (5 papers). Shashi Narayan is often cited by papers focused on Natural Language Processing Techniques (23 papers), Topic Modeling (22 papers) and Advanced Text Analysis Techniques (5 papers). Shashi Narayan collaborates with scholars based in United Kingdom, United States and France. Shashi Narayan's co-authors include Shay B. Cohen, Mirella Lapata, Claire Gardent, Anastasia Shimorina, Laura Perez-Beltrachini, Joshua Maynez, Maximin Coavoux, Yao Zhao, Ryan McDonald and Vitaly Nikolaev and has published in prestigious journals such as Pattern Recognition Letters, Computational Linguistics and Transactions of the Association for Computational Linguistics.

In The Last Decade

Shashi Narayan

28 papers receiving 1.4k citations

Hit Papers

Don’t Give Me the Details, Just the Summary! Topic-Aware ... 2018 2026 2020 2023 2018 200 400 600
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.

  1. 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 — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shashi Narayan United Kingdom 14 1.3k 174 113 86 53 32 1.5k
Mikel Artetxe Spain 10 1.4k 1.1× 337 1.9× 115 1.0× 49 0.6× 44 0.8× 35 1.6k
Sebastian Ruder United States 17 1.3k 1.0× 292 1.7× 142 1.3× 45 0.5× 34 0.6× 46 1.5k
Steffen Eger Germany 14 816 0.6× 142 0.8× 125 1.1× 39 0.5× 28 0.5× 74 993
Jackie Chi Kit Cheung Canada 16 919 0.7× 111 0.6× 93 0.8× 55 0.6× 59 1.1× 76 1.0k
Claire Gardent France 17 1.1k 0.9× 139 0.8× 108 1.0× 62 0.7× 42 0.8× 110 1.3k
Mohammad Taher Pilehvar United Kingdom 20 1.4k 1.0× 141 0.8× 114 1.0× 115 1.3× 32 0.6× 59 1.5k
Swabha Swayamdipta United States 12 885 0.7× 132 0.8× 98 0.9× 53 0.6× 22 0.4× 31 1.0k
Chandra Bhagavatula United States 14 1.0k 0.8× 263 1.5× 150 1.3× 47 0.5× 49 0.9× 38 1.2k
Tushar Khot United States 18 1.2k 0.9× 342 2.0× 181 1.6× 63 0.7× 56 1.1× 48 1.4k

Countries citing papers authored by Shashi Narayan

Since Specialization
Citations

This map shows the geographic impact of Shashi Narayan'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 Shashi Narayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shashi Narayan more than expected).

Fields of papers citing papers by Shashi Narayan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shashi Narayan. 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 Shashi Narayan. The network helps show where Shashi Narayan may publish in the future.

Co-authorship network of co-authors of Shashi Narayan

This figure shows the co-authorship network connecting the top 25 collaborators of Shashi Narayan. A scholar is included among the top collaborators of Shashi Narayan 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 Shashi Narayan. Shashi Narayan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Amplayo, Reinald Kim, Fantine Huot, Nicola De Cao, et al.. (2024). Learning to Plan and Generate Text with Citations. 11397–11417. 3 indexed citations
3.
Huot, Fantine, Joshua Maynez, Chris Alberti, et al.. (2024). 𝜇PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge. 2146–2163.
4.
Zablotskaia, Polina, et al.. (2023). On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. 2980–2992. 2 indexed citations
5.
Narayan, Shashi, Joshua Maynez, Reinald Kim Amplayo, et al.. (2023). Conditional Generation with a Question-Answering Blueprint. Transactions of the Association for Computational Linguistics. 11. 974–996. 12 indexed citations
6.
Huot, Fantine, Joshua Maynez, Shashi Narayan, et al.. (2023). Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation. 105–116.
7.
Amplayo, Reinald Kim, Kellie Webster, Michael J. Collins, Dipanjan Das, & Shashi Narayan. (2023). Query Refinement Prompts for Closed-Book Long-Form QA. 7997–8012.
8.
Aharoni, Roee, Shashi Narayan, Joshua Maynez, et al.. (2023). Multilingual Summarization with Factual Consistency Evaluation. 3562–3591. 8 indexed citations
9.
Liu, Yongtai, et al.. (2022). Data Augmentation for Low-Resource Dialogue Summarization. 703–710. 3 indexed citations
10.
Narayan, Shashi & Claire Gardent. (2020). Deep Learning Approaches to Text Production. 13(1). 1–199. 7 indexed citations
11.
Coavoux, Maximin, Shashi Narayan, & Shay B. Cohen. (2018). Privacy-preserving Neural Representations of Text. arXiv (Cornell University). 1–10. 61 indexed citations
12.
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 →
13.
Gardent, Claire & Shashi Narayan. (2018). Deep Learning Approaches to Text Production. Figshare. 4–9. 2 indexed citations
14.
Ribeiro, Joana, Shashi Narayan, Shay B. Cohen, & Xavier Carreras. (2018). Local String Transduction as Sequence Labeling. Zenodo (CERN European Organization for Nuclear Research). 1360–1371. 8 indexed citations
15.
Gardent, Claire, Anastasia Shimorina, Shashi Narayan, & Laura Perez-Beltrachini. (2017). Creating Training Corpora for NLG Micro-Planners. Edinburgh Research Explorer (University of Edinburgh). 179–188. 201 indexed citations
16.
Narayan, Shashi & Shay B. Cohen. (2016). Optimizing Spectral Learning for Parsing. Edinburgh Research Explorer. 1546–1556. 3 indexed citations
17.
Narayan, Shashi, Claire Gardent, Shay B. Cohen, & Anastasia Shimorina. (2015). The 2017 Conference on Empirical Methods on Natural Language Processing (EMNLP). 181 indexed citations
18.
Narayan, Shashi & Shay B. Cohen. (2015). Diversity in Spectral Learning for Natural Language Parsing. Edinburgh Research Explorer. 1868–1878. 7 indexed citations
19.
Makhija, Sonia K., et al.. (2008). Etiological profile of stroke and its relation with prothrombotic states. The Indian Journal of Pediatrics. 75(6). 579–584. 11 indexed citations
20.
Hon, Hsiao-Wuen, et al.. (2002). Towards large vocabulary Mandarin Chinese speech recognition. i. I/545–I/548. 16 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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