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Igor Shalyminov

Igor Shalyminov has 2 papers in our library, in 2024, most of them on Implicature, Summarization, English; often with Hang Su, Hwanjun Song, Saab Mansour.

2 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 9 · 42 works · 230 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 64 times in our library · ⭐ Paper Stars ranking: #5106

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢AmazonSeattle · United States2023–2025 · 13 paperscurrent🎓Heriot-Watt UniversityEdinburgh · United Kingdom2017–2021 · 9 papers🏢MicrosoftRedmond · United States2018–2019 · 3 papers🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2024 · 2 papers🎓The University of Texas at AustinAustin · United States2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Summarization · 1 workEnglish · 1 workImplicature · 1 work

 Works by year

2020s

20242 papersIgor Shalyminov's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. FineSurE: Fine-grained Summarization Evaluation using LLMs (2024 · 91 citations)
    Evaluating summaries automatically remains difficult. ROUGE-style overlap metrics track human judgment poorly, and recent LLM judges such as G-Eval assign a single Likert score per summary, which hides where and how a summary fails. FineSurE addresses this by having an LLM run two fine-grained tasks. In fact…
  2. Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024 · 4 citations)
    Intent embedding models (for chatbots that map «I want to order a pizza» to order_food) are tested on two things standard benchmarks miss: negation («I don't want to order pizza», a different intent with high word overlap) and implicature («I'm really hungry», the same intent with little overlap).

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 64 times (of the 141,840 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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