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Xuedong Huang

Xuedong Huang has 2 papers in our library, in 2018–2021, most of them on Machine translation, Summarization, Evaluation & benchmarks; often with Chenguang Zhu, Christian Federmann, Hany Hassan.

2 papers in our library · 2018–2021

OpenAlex · ORCID · h-index 43 · 135 works · 9,235 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 343 times in our library · ⭐ Paper Stars ranking: #2232

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

🎓Beihang UniversityBeijing · China2025 · 1 papercurrent🏢MicrosoftRedmond · United States1994–2024 · 58 papers🎓Carnegie Mellon UniversityPittsburgh · United States1989–2002 · 26 papers🎓University of EdinburghEdinburgh · United Kingdom1988–1992 · 4 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.

 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.Evaluation & benchmarks · 1 workMachine translation · 1 workSummarization · 1 work

 Works by year

2020s

20211 paperEnhancing Factual Consistency of Abstractive Summarization

2010s

20181 paperAchieving Human Parity on Automatic Chinese to English News Translation

 Papers

The most cited first (OpenAlex and our library).

  1. Achieving Human Parity on Automatic Chinese to English News Translation (2018 · 797 citations)
    The paper asks whether machine translation can match professional human translators, and first makes that question testable.
  2. Enhancing Factual Consistency of Abstractive Summarization (2021 · 188 citations)
    Abstractive summarizers often produce text that misstates or invents facts from the source. Prior work estimates that up to 30% of such outputs contain these errors, which undermines trust in them. The authors address this in two ways. First, FASum is a transformer encoder-decoder summarizer that builds a per-article…

 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 343 times (of the 141,840 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated

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