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Itsumi Saito

Itsumi Saito has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Question answering, Summarization; often with Kosuke Nishida, Kyosuke Nishida, Kuniko Saito.

2 papers in our library · 2023–2026

OpenAlex · h-index 12 · 32 works · 527 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 117 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7637

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

🎓Tohoku UniversitySendai · Japan2024–2026 · 3 paperscurrent🏢NTTTokyo · Japan2002–2023 · 20 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 5Research subjects and languages the work is about.Question answering · 1 workVision & language · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 workSummarization · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images (2023 · 116 citations)
    Most document VQA benchmarks assume that one page holds everything needed to answer a question. Real documents rarely work that way, so SlideVQA tests whether systems can combine information spread across several pages. Each item pairs a question with a 20-slide deck taken from SlideShare. A system must find the…
  2. InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization (2026)
    LLMs tuned to satisfy users often pad their answers with unrequested material. This is a serious flaw in summarization, where brevity is the point, and existing instruction-following benchmarks rarely address it. The authors present InstructSum, an English benchmark built from Wikipedia. Article abstracts serve as…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetKosuke Nishida et al.2019 · 127 citations
  2. SlideVQA: A Dataset for Document Visual Question Answering on Multiple ImagesProceedings of the AAAI Conference on Artificial IntelligenceRyota Tanaka, Kyosuke Nishida, Kosuke Nishida, et al.2023 · 116 citations
  3. Multi-style Generative Reading Comprehension ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetKyosuke Nishida et al.2019 · 98 citations
  4. InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in SummarizationLanguage Resources and Evaluation Conference (LREC)Kosuke Nishida, Kyosuke Nishida, Itsumi Saito2026

 Co-authors here

3 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 117 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated

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