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Dejiao Zhang

Dejiao Zhang has 3 papers in our library, in 2021, most of them on Summarization, Question answering; often with Bing Xiang, Feng Nan, Henghui Zhu.

3 papers in our library · 2021

OpenAlex · h-index 14 · 50 works · 835 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 198 times in our library · ⭐ Paper Stars ranking: #3134

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 States2020–2024 · 18 papers🎓Columbia UniversityNew York · United States2020–2021 · 7 papers🎓University of MichiganAnn Arbor · United States2015–2018 · 7 papers🎓University of North Carolina at Chapel HillChapel Hill · United States2023 · 2 papers🎓Chinese University of Hong KongHong Kong · Hong Kong2020–2021 · 2 papers🎓Nanjing University of Information Science and TechnologyNanjing · China2013–2014 · 2 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Summarization · 2 worksQuestion answering · 1 work

 Works by year

2020s

20213 papersDejiao Zhang's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Supporting Clustering with Contrastive Learning (2021 · 239 citations)
    Deep clustering methods that rely on distances often fail when semantic categories overlap heavily in the embedding space at the start of training.
  2. Entity-level Factual Consistency of Abstractive Text Summarization (2021 · 205 citations)
    Abstractive summarizers often hallucinate named entities, inserting people, places or organizations that never occur in the source article. ROUGE does not detect this kind of error. The authors address it with three entity-level metrics computed with spaCy NER and lenient, case-insensitive n-gram matching:
  3. Improving Factual Consistency of Abstractive Summarization via Question Answering (2021 · 128 citations)
    Abstractive summarizers such as fine-tuned BART-large often produce fluent summaries that are not supported by the source document or even contradict it. Standard MLE training rewards n-gram overlap, not faithfulness. The authors tackle this problem in two steps.

 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 198 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

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