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Rahul Jha

Rahul Jha has 2 papers in our library, in 2021, most of them on Summarization, Evaluation & benchmarks; often with Asli Celikyilmaz, Ahmed Awadallah, Da Yin.

2 papers in our library · 2021

OpenAlex · ORCID · h-index 13 · 40 works · 793 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 281 times in our library · ⭐ Paper Stars ranking: #3272

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

🎓Tribhuvan UniversityKathmandu · Nepal2023–2024 · 14 paperscurrent🏢MicrosoftRedmond · United States2016–2021 · 8 papers🎓University of MichiganAnn Arbor · United States2013–2016 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 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 2Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksSummarization · 2 works

 Works by year

2020s

20212 papersRahul Jha's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021 · 400 citations)
    Summaries that cover a whole meeting tend to miss what individual readers need. Meetings are long, involve several speakers and shift between topics, and readers usually want something specific: a decision, one person's opinion, or one discussion thread. The authors propose a new task, query-based multi-domain…
  2. GO FIGURE: A Meta Evaluation of Factuality in Summarization (2021 · 129 citations)
    Neural summarizers often produce fluent text that misstates or invents facts. Many new automatic metrics aim to detect such errors, but there has been no systematic way to check whether these metrics actually work. GO FIGURE is a meta-evaluation framework that tests factuality metrics against five requirements…

 Co-authors here

12 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 281 times (of the 141,840 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated

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