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Atsushi Keyaki

Atsushi Keyaki has 3 papers in our library, in 2026, most of them on Summarization; often with Daisuke Kawahara, Hideyuki Tachibana, Hirokazu Kiyomaru.

3 papers in our library · 2026

OpenAlex · ORCID · h-index 5 · 174 works · 90 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11362

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

🎓Hitotsubashi UniversityTokyo · Japan2023–2026 · 13 paperscurrent🎓Tokyo Institute of TechnologyTokyo · Japan2014–2019 · 12 papers🏢Denso IT Laboratory Inc.Tokyo · Japan2020–2022 · 7 papers🎓Doshisha UniversityKyoto · Japan2009–2011 · 6 papers🏢DensoSouthfield · United States2020–2022 · 4 papers🎓Nara Institute of Science and TechnologyIkoma · Japan2011–2013 · 3 papers

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

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksSocial Sciences · 2 worksSociology and Political Science · 2 worksPolitical Science and International Relations · 1 work

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Constructing a Japanese Claim Decomposition Dataset for Fact-Checking of LLM-Generated Texts (2026 · 1 citation)
    LLM outputs can contain hallucinations. A common way to fact-check them is to first split the text into atomic, self-contained claims, then retrieve evidence for each claim and predict a verdict. How well the text is split strongly affects the final checking accuracy. Earlier resources mostly had annotators review…
  2. Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels (2026)
    Japanese has few manually annotated benchmarks for checking whether LLM output stays grounded in its input.
  3. Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026)
    Fact-checking pipelines for LLM output usually end with verdict prediction, in which a claim is compared against retrieved evidence.

 Co-authors here

10 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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