Esin Durmus

3.8k total citations · 2 hit papers
18 papers, 432 citations indexed

About

Esin Durmus is a scholar working on Artificial Intelligence, Communication and Sociology and Political Science. According to data from OpenAlex, Esin Durmus has authored 18 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Communication and 4 papers in Sociology and Political Science. Recurrent topics in Esin Durmus's work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Social Media and Politics (4 papers). Esin Durmus is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Social Media and Politics (4 papers). Esin Durmus collaborates with scholars based in United States, Hong Kong and Italy. Esin Durmus's co-authors include Faisal Ladhak, Tatsunori Hashimoto, Kathleen McKeown, Dan Jurafsky, Claire Cardie, Myra Cheng, Tianyi Zhang, Percy Liang, Aylin Caliskan and Federico Bianchi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, ACM Computing Surveys and Transactions of the Association for Computational Linguistics.

In The Last Decade

Esin Durmus

16 papers receiving 414 citations

Hit Papers

Benchmarking Large Langua... 2023 2026 2024 2024 2023 40 80 120
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. Benchmarking Large Language Models for News Summarization
    2024 124 citations Tianyi Zhang, Faisal Ladhak et al. Transactions of the Association for Computational Linguistics profile →
  2. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
    2023 118 citations Federico Bianchi, Pratyusha Kalluri et al. BOA (University of Milano-Bicocca) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Esin Durmus United States 10 280 51 45 40 32 18 432
Nancy Fulda United States 6 279 1.0× 39 0.8× 76 1.7× 36 0.9× 26 0.8× 22 455
Nan‐Chen Chen United States 7 139 0.5× 71 1.4× 34 0.8× 50 1.3× 41 1.3× 11 280
Faisal Ladhak United States 9 303 1.1× 74 1.5× 27 0.6× 37 0.9× 23 0.7× 14 443
Li Lucy United States 7 163 0.6× 15 0.3× 50 1.1× 29 0.7× 20 0.6× 14 301
Annette Hautli-Janisz Germany 9 227 0.8× 48 0.9× 21 0.5× 21 0.5× 26 0.8× 40 346
Jonathan Stray United States 6 78 0.3× 50 1.0× 88 2.0× 49 1.2× 24 0.8× 11 251
Lucy Vasserman United States 7 529 1.9× 33 0.6× 100 2.2× 83 2.1× 80 2.5× 8 635
Lilja Øvrelid Norway 16 691 2.5× 18 0.4× 61 1.4× 20 0.5× 75 2.3× 69 799
Traian Rebedea Romania 11 251 0.9× 26 0.5× 55 1.2× 14 0.3× 126 3.9× 80 429
Daniel Hershcovich Denmark 12 570 2.0× 52 1.0× 28 0.6× 11 0.3× 103 3.2× 54 646

Countries citing papers authored by Esin Durmus

Since Specialization
Citations

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

Fields of papers citing papers by Esin Durmus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Esin Durmus

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

All Works

18 of 18 papers shown
1.
Yang, Xiaocheng, Zhuo Cheng, Esin Durmus, et al.. (2026). Must Read: A Comprehensive Survey of Computational Persuasion. ACM Computing Surveys.
2.
Gligorić, Kristina, Pratyusha Kalluri, Esin Durmus, et al.. (2024). People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise. Proceedings of the National Academy of Sciences. 121(38). e2322764121–e2322764121. 5 indexed citations
3.
Zhang, Tianyi, Faisal Ladhak, Esin Durmus, et al.. (2024). Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics. 12. 39–57. 124 indexed citations breakdown →
4.
Gligorić, Kristina, Myra Cheng, Lucia Zheng, Esin Durmus, & Dan Jurafsky. (2024). NLP Systems That Can’t Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps. 5942–5959. 2 indexed citations
5.
Bianchi, Federico, Pratyusha Kalluri, Esin Durmus, et al.. (2023). Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. BOA (University of Milano-Bicocca). 1493–1504. 118 indexed citations breakdown →
6.
Cheng, Myra, Esin Durmus, & Dan Jurafsky. (2023). Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models. 1504–1532. 39 indexed citations
7.
Ladhak, Faisal, Esin Durmus, Mirac Süzgün, et al.. (2023). When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization. 3206–3219. 15 indexed citations
8.
Durmus, Esin, et al.. (2023). Towards Reference-free Text Simplification Evaluation with a BERT Siamese Network Architecture. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 13250–13264.
9.
Wang, Tianshu, Faisal Ladhak, Esin Durmus, & He He. (2022). Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. 11913–11921. 3 indexed citations
10.
Ladhak, Faisal, Esin Durmus, He He, Claire Cardie, & Kathleen McKeown. (2022). Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1410–1421. 38 indexed citations
11.
Durmus, Esin, Faisal Ladhak, & Tatsunori Hashimoto. (2022). Spurious Correlations in Reference-Free Evaluation of Text Generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1443–1454. 11 indexed citations
12.
Durmus, Esin, et al.. (2021). Leveraging Topic Relatedness for Argument Persuasion. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4401–4407. 1 indexed citations
13.
Durmus, Esin, et al.. (2020). Exploring the Role of Argument Structure in Online Debate Persuasion. 8905–8912. 21 indexed citations
14.
Nissim, Malvina, Viviana Patti, Barbara Plank, & Esin Durmus. (2020). Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media. Data Archiving and Networked Services (DANS). 25 indexed citations
15.
Durmus, Esin, et al.. (2019). Persuasion of the Undecided: Language vs. the Listener. 167–176. 12 indexed citations
16.
Durmus, Esin & Claire Cardie. (2019). Modeling the Factors of User Success in Online Debate. 2701–2707. 12 indexed citations
17.
Durmus, Esin & Claire Cardie. (2018). Understanding the Effect of Gender and Stance in Opinion Expression in Debates on "Abortion". 69–75. 3 indexed citations
18.
Niculae, Vlad, Xilun Chen, Cheng Yao, et al.. (2016). Cornell Belief and Sentiment System at TAC 2016.. Theory and applications of categories. 3 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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